A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) has been proposed and achieved success for spatial image steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its antidetectability and training efficiency should be improved. In conventional steganography, research has shown that the side information calculated from the precover can be used to enhance security. However, it is hard to calculate the side information without the spatial domain image. In this work, an embedding cost learning framework for JPEG image steganography via a GAN (JS–GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side information (ESI). Experimental results have demonstrated that the proposed method can automatically learn a content‐adaptive embedding cost function, and using the ESI properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with a quality factor of 75 and 0.4 bpnzAC, the proposed JS–GAN can increase the detection error by 2.58% over J‐UNIWARD, and the ESI–aided version JS–GAN (ESI) can further increase the security performance by 11.25% over JS–GAN.

With the advancement of 5G networks, the deploy-ment of Internet of Things (IoT) technology has seen significant growth. Blockchain technology, recognized for its strong security features, is increasingly utilized within the IoT domain. However, the current IoT landscape is characterized by challenges such as substantial resource consumption, limited throughput capacity, and insufficient security protocols, which hinder its optimal per-formance. Towards addressing such problems, we propose a con-sensus algorithm called Proof-of-GoS (PoG) based on the grade of service (GoS), in which a node must have a service score over a set score threshold to be allowed to join the consensus process. The correct behavior of a node results in a reward, while any malicious actions result in penalties. Finally, we simulate a network to evalu-ate the performance and security of PoG and compare it with sev-eral existing consensus algorithms. The experimental findings in-dicate that the proposed PoG consensus algorithm retains the fun-damental security properties of blockchain and outperforms the state-of-the-art consensus mechanisms.

Reversible data hiding-based contrast enhancement can be applied to medical images, which not only allows the storage of patient information through reversible embedding, but also achieves image contrast enhancement, thereby assisting doctors in accurately diagnosing patient diseases. In response to the existing problems of mainstream methods, a novel reversible data hiding-based local contrast enhancement method for medical images is proposed. This method utilizes superpixel segmentation to segment medical images into multiple pixel blocks, and performs reversible data embedding and contrast enhancement for the pixel blocks within the region of interest (ROI). Additionally, a new embedding strategy is proposed. According to the contrast and texture features of each pixel block, histogram expansion of different degrees is carried out to effectively enhance the pixel blocks with low contrast, while avoiding excessive enhancement of the pixel blocks with high contrast. Experimental results demonstrate that, compared with the state-of-the-art mainstream methods, the proposed method not only improves the contrast in the ROI but also ensures high visual quality of the medical images.

Light-field (LF) images contain rich visual information and have broader application scenarios than traditional images. However, their complex structure also makes their copyright protection more challenging. Currently, there are few watermarking schemes suitable for LF images, and most of them fail to restore the original image after embedding the watermark. In addition, geometric attacks remain a difficult problem in the field of LF image watermarking. In this study, we propose a multiple reversible robust LF image watermarking scheme based on code division multiplexing (CDM) and quaternion polar harmonic Fourier moments (QPHFMs). This scheme embeds multiple identical watermarks into the LF macro-pixel image, and the compensation information for information loss caused by watermark embedding is reversibly embedded into the LF sub-aperture images. The watermark can be extracted and the original LF image can be fully recovered if the image has not been attacked. The watermark can be extracted to verify the copyright ownership of the LF image even when the image has been attacked. Experimental results demonstrate that the proposed watermarking scheme is resistant to various attacks and exhibits strong robustness.

In real-world applications, screen capturing represents a significant scenario where this process can induce substantial distortion to the original image. Previous methods for simulating screen-shooting distortion often involved combining different formulas. We found that these simulation methods still have a significant gap compared to real distortions, making it urgently necessary to develop a realistic and credible comprehensive noise layer to achieve robustness against screen-shooting distortion. This paper presents a watermarking scheme capable of withstanding severe screen-shooting distortion. First, a dataset is constructed to train a screen-shooting distortion simulation network based on style transfer. Subsequently, a comprehensive noise layer is built upon this network to achieve robustness against severe screen-shooting distortion. Additionally, this paper incorporates structural re-parameterization techniques into the traditional U-shaped encoder to improve the quality of encoded images. Extensive experiments demonstrate the proposed scheme’s superior performance in terms of robustness and generalization, especially under severe screen-shooting distortion conditions.

Purely chromatic background images are widely used in computer wallpapers and advertisements, leading to issues such as copyright infringement and the loss of interest of holders. Image hashing is a technique used for comparing the similarity between images, and is often used for image verification, search, and copy detection due to its insensitivity to subtle changes in the original image. In a purely chromatic background image, the central detail of the image is the primary part and the key for copyright authentication. As the perception hash (pHash) algorithm only retains the low-frequency portion of the discrete cosine transform (DCT) matrix, it is unsuitable for purely chromatic background images. To deal with this issue, we propose an improved perception hash (ipHash) algorithm to enhance the universality of the algorithm by extracting purely chromatic background image features. Meanwhile, the development of image hashing is restricted due to the requirement of a trusted third party. To solve this issue, a secure blockchain-based image copyright protection scheme is designed. It realizes the copyright authentication and traceability, and overcomes the issue of a lack of trusted third parties. Experimental results show that the proposed method outperforms the state-of-theart image copyright protection schemes.

With the growing popularity of high-resolution (HR) video and the continuous growth of network bandwidth, the challenge of object removal detection in HR videos has attracted significant attention. Expert forgers leverage the rich detail in HR videos for meticulous pixel manipulation and apply sophisticated postprocessing techniques to hide high-frequency artifacts, thereby making forgery detection and localization more difficult when existing schemes are used. Additionally, the end-to-end framework simplifies the detection and localization process, which has not been considered in previous work. To solve the above issues, a spatiotemporal encoder−decoder network (SEDN) is proposed for end-to-end object removal forgery detection in HR videos. In the SEDN, a new model composed of a 3D asymmetric dual-stream network (3D-ADSN) and Transformer is proposed. The 3D-ADSN is utilized as the encoder, which fully integrates the high-frequency and low-frequency spatiotemporal information of videos. Transformer is utilized as the decoder to capture the global structure spatiotemporal information of the long-range feature sequence obtained by the encoder. This network combination successfully achieves simultaneous detection in the temporal and spatial domains without any additional postprocessing calculations. The experimental results demonstrate the better performance of the SEDN at different resolutions.

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably usingk-out-of-nmarked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable.

Deep Image Hiding (DIH) aims to imperceptibly hide images within image. To improve its security performance, some DIH methods design Security Metrics (SMs) to guide the learning of their hiding networks. However, these methods focus on optimizing their anti-steganalysis ability on specific SMs, resulting in inferior generalization ability. To overcome these limitations, in this paper, we introduce meta-learning into DIH and propose Meta Security Metric-based DIH (MSM-DIH). In the MSM-DIH, the Invertible Neural Network (INN)-based hiding network is learned under the guidance of a learnable meta SM generalized from multiple fixed source SMs, and each SM is composed of a metric network and a contrastive loss function. Specifically, MSM-DIH is trained with bi-level optimization. In the outer optimization, a meta SM is learned to assign higher security scores for more advanced stego images. Besides, the domain knowledge of steganalysis is transferred from the multiple pre-trained source metric networks to the meta metric network, so as to enhance the generalization ability of the meta SM. In the inner optimization, the hiding network is learned to generate more secure stego images according to the learned meta SM. Experimental results show that our MSM-DIH has achieved the best security performance in most cases.

Reversible data hiding-based contrast enhancement (RDHCE) can be used in contrast enhancement for medical images, and it has been a popular research topic in recent years. However, the existing RDHCE methods suffer from the problem of inaccurate segmentation of the region of interest (ROI) in medical images, which can impact the contrast enhancement effect of the images. Moreover, some methods face limitations in their universality for ROI histograms with few empty bins on both sides, which results in unsatisfactory embedding capacity and contrast enhancement effect. To solve these problems, this study proposes an improved RDHCE method for medical images. The proposed method uses the UNet3+ network model, which makes the segmented ROI histograms more consistent with the subjective judgment of doctors compared to those obtained by traditional segmentation approaches. In addition, a multi-group stretching method is proposed to address the limitation of histogram expansion caused by the empty bins on both histogram sides, enabling adaptation to different ROI histograms with varying gray distributions. Compared to state-of-the-art RDHCE methods, the proposed method offers better generalizability, superior contrast enhancement performance and a larger ROI embedding capacity. It can greatly improve the visual quality of medical images in the field of medical imaging and aid doctors in making more accurate diagnoses.

With the development of data security and privacy requirements in the field of cloud computing, Reversible Data Hiding in Encrypted Images (RDHEI) in encryption domain has received increasing attention. In order to take full advantage of the spatial and textural features of the original image, reversible data hiding in encrypted image with adaptive Huffman code based on Dynamic Prediction Axes (RDHEI-HDA) is proposed. First, the prediction errors of the original plaintext image are calculated according to the multidirectional median edge detector (M-MED) combined with the Dynamic Prediction Axes which are generated by the spatial correlation of the original image. After encryption process with the stream cipher, the adaptive Huffman coding labeling rule is created for pixel labeling and classification according to the Dynamic Prediction Axes and the distribution of prediction errors. Finally, bit substitution is employed to insert secret data and side information into the image. In Comparison to most of the state-of-the-art RDHEI methods, the experimental results show that the RDHEI-HDA method provides a higher pure payload while ensuring safety.

Image encryption is used for privacy protection in cloud computing. Nowadays, reversible data hiding in encrypted image (RDHEI) has achieved great success with the demand of embedding additional information into encrypted image. The existing algorithms cannot implement large embedding capacity and good reconstructed image quality simultaneously. Besides, the universality of some methods is limited when they are used in images with different textural characteristics. In this work, an RDHEI method based on most significant bit (MSB) prediction and error embedding is proposed. On one hand, all types of prediction errors are considered in the proposed method, therefore all the pixels with prediction errors can be recovered correctly. On the other hand, error blocks are utilized to mark the locations of prediction errors and message blocks are utilized to embed data. Moreover, flag blocks are utilized to distinguish error and message blocks. To solve the problem of misjudgement for flag blocks in the decoding phase, special operations are conducted on error blocks and message blocks. Experimental results demonstrate that, compared with the state-of-the-art RDHEI methods, the proposed method has good universality on well-known databases.

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent space with multivariate Gaussian distribution and image space with a complex distribution. In the process of S2I transformation, guided by a given secret message, we construct a latent vector and then map it to a generated image by the Glow model, so that the secret message is finally transformed to the generated image. Owing to good efficiency and reversibility of S2IRT scheme, the proposed steganographic approach achieves both high hiding capacity and accurate extraction of secret message from generated image. Furthermore, a separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the robustness to common image attacks. The experiments demonstrate the proposed steganographic approaches can achieve high hiding capacity (up to 4bpp) and accurate information extraction (almost 100% accuracy rate) simultaneously, while maintaining desirable anti-detectability and imperceptibility.

In recent years, generative adversarial networks (GANs) have been widely used to generate realistic fake face images, which can easily deceive human beings. To detect these images, some methods have been proposed. However, their detection performance will be degraded greatly when the testing samples are post-processed. In this paper, some experimental studies on detecting post-processed GAN-generated face images find that (a) both the luminance component and chrominance components play an important role, and (b) the RGB and YCbCr color spaces achieve better performance than the HSV and Lab color spaces. Therefore, to enhance the robustness, both the luminance component and chrominance components of dual-color spaces (RGB and YCbCr) are considered to utilize color information effectively. In addition, the convolutional block attention module and multilayer feature aggregation module are introduced into the Xception model to enhance its feature representation power and aggregate multilayer features, respectively. Finally, a robust dual-stream network is designed by integrating dual-color spaces RGB and YCbCr and using an improved Xception model. Experimental results demonstrate that our method outperforms some existing methods, especially in its robustness against different types of post-processing operations, such as JPEG compression, Gaussian blurring, gamma correction, and median filtering.

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods.

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility.

Some research progress has been made on fractional-order continuous orthogonal moments (FrCOMs) in the past two years. Compared with integer-order continuous orthogonal moments (InCOMs), FrCOMs increase the number of affine invariants and effectively improve numerical stability. However, the existing types of FrCOMs are still very limited, of which all are planar image oriented. No report on stereoscopic images is available yet. To this end, in this paper, FrCOMs corresponding to various types of InCOMs are first deduced, and then, they are combined with trinion theory to construct trinion FrCOMs (TFrCOMs) applicable to stereoscopic images. Furthermore, the reconstruction performance and geometric invariance of TFrCOMs are analyzed theoretically and experimentally. Finally, an application in the stereoscopic image zero-watermarking algorithm is investigated to verify the superior performance of TFrCOMs.

In this letter, we propose a general digital image operation anti-forensic framework based on generative adversarial nets (GANs), called dual-domain generative adversarial network (DDGAN). To tackle the issue of image operation detection, the proposed framework incorporates both operation specific forensic features and machine-learned knowledge to ensure that the generated images exhibit better undetectability performance against various detectors. The DDGAN consists of a generator and two discriminators working on different domains, i.e., the operation-specific feature domain which helps to conceal the artifacts from the perspective of forensic analysis for the target task, and the spatial domain which facilitates to take advantage of machine-learned features from the scratch as a supplementary. Through the experiments on median filtering and JPEG compression anti-forensics, we show the superior performance of the proposed DDGAN compared with state-of-the-art anti-forensic methods in terms of undetectability and visual quality.

With the rapid development of network media, increasing research on reversible watermarking has focused on improving its robustness to resisting attacks during digital media transmission. There are some other reversible watermarking schemes that work in the encrypted domain for preserving the privacy of the cover image. The robustness of the watermarking and the privacy preserving of the cover image have become the key factors of reversible watermarking. However, there are few robust reversible watermarking schemes in the encrypted domain that could resist common attacks (such as JPEG compression, noise addition) and preserve privacy at the same time. In addition, the embedding capacity of a robust watermark and the efficiency of the encryption method must be considered. Recently, cloud computing technology has led to the rapid growth of network media, and many multimedia properties are owned by multiple parties, such as a film’s producer and multiple distributors. Multi-party watermarking has become an important demand for network media to protect all parties’ rights. In this paper, a Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed. Additive secret sharing and block-level scrambling are developed to generate the encrypted image. Then, the robust reversible watermarking based on significant bit Prediction Error Expansion (PEE) is performed by Secure Multi-party Computation (SMC). For applications with high robustness, a Modified RRWEI-SM is proposed by exploiting two-stage architecture. Furthermore, both the RRWEI-SM scheme and Modified RRWEI-SM scheme are separable and can be applied to multiparty copyright protection. The experimental results and theoretical analysis demonstrate here that the RRWEI-SM and the Modified RRWEI-SM are secure, robust and effective.

As one of the most important topics in image forensics, resampling detection has developed rapidly in recent years. However, the robustness to JPEG compression is still challenging for most classical spectrum-based methods, since JPEG compression severely degrades the image contents and introduces block artifacts in the boundary of the compression grid. In this article, we propose a method to estimate the upscaling factors on double JPEG compressed images in the presence of image upscaling between the two compressions. We first analyze the spectrum of scaled images and give an overall formulation of how the scaling factors along with the parameters of JPEG compression and image contents influence the appearance of tampering artifacts. The expected positions of five kinds of characteristic peaks are analytically derived. Then, we analyze the features of double JPEG compressed images in the block discrete cosine transform (BDCT) domain and present an inverse scaling strategy for the upscaling factor estimation with a detailed proof. Finally, a fusion method is proposed that through frequency-domain analysis, a candidate set of upscaling factors is given, and through analysis in the BDCT domain, the optimal estimation from all candidates is determined. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

Realizing autonomous vehicles is one of the ultimate dreams for humans. However, perceptual information collected by sensors in dynamic and complicated environments, in particular, vision information, may exhibit various types of degradation. This may lead to mispredictions of context followed by more severe consequences. Thus, it is necessary to improve degraded images before employing them for context prediction. To this end, we propose a generative adversarial network to restore images from common types of degradation. The proposed model features a novel architecture with an inverse and a reverse module to address additional attributes between image styles. With the supplementary information, the decoding for restoration can be more precise. In addition, we develop a loss function to stabilize the adversarial training with better training efficiency for the proposed model. Compared with several state-of-the-art methods, the proposed method can achieve better restoration performance with high efficiency. It is highly reliable for assisting in context prediction in autonomous vehicles.

The emergence of adversarial examples has had a significant impact on the development and application of deep learning. In this paper, a novel convolutional neural network model, the stochastic multifilter statistical network (SmsNet), is proposed for the detection of adversarial examples. A feature statistical layer is constructed to collect statistical data of feature map output from each convolutional layer in SmsNet by combining manual features with a neural network. The entire model is an end-to-end detection model, so the feature statistical layer is not independent of the network, and its output is directly transmitted to the fully connected layer by a short-cut connection called the SmsConnection. Additionally, a dynamic pruning strategy is introduced to simplify the model structure for better performance. The experiments demonstrate the effectiveness of the network structure and pruning strategy, and the proposed model achieves high detection rates against state-of-the-art adversarial attacks.

The spatial correlation of the intra-frame prediction units brings great challenges when minimizing embedding distortions using syndrome-trellis coding (STC) in High Efficiency Video Coding (HEVC) steganography. To solve this problem, we propose a layered embedding scheme which embeds information into the intra-prediction modes (IPMs) of 4×4 intra-frame prediction units (PUs) in HEVC. Firstly we divide the PUs of the intra-frame into different layers using Hasse diagram and make modification decisions for PUs in each layer respectively to decorrelate the correlated PUs. Secondly we make a statistics on more than 100,000 sampling PU pairs to quantitatively analyze the impacts between the distortions of PUs and then design a distortion function which takes mutual impacts of PUs into account. Experimental results show that our method can significantly reduce the embedding distortion and improve the security compared with the existing STC-based steganography methods embedding in IPMs.

An efficient predictor is crucial for high embedding capacity and low image distortion. In this letter, a ridge regression-based high precision error prediction algorithm for reversible data hiding is proposed. The ridge regression is a penalized least-square algorithm, which solves the overfitting problem of the least-square method. Reversible data hiding based on ridge regression predictor minimizes the residual sum of squares between predicted and target pixels subject to the constraint expressed in terms of the L2-norm. Compared to a least-square-based predictor, the ridge regression-based predictor can obtain more small prediction errors, proving that the proposed method has a higher accuracy. In addition, the eight neighbor pixels of the target pixels and their two different combinations are selected as training and support sets, respectively. This selection scheme further improves the prediction accuracy. Experimental results show that the proposed method outperforms state-of-the-art adaptive reversible data hiding in terms of prediction accuracy and embedding performance.

In many reversible data hiding (RDH) methods for halftone images, the traditional embedding process embeds a 1-bit secret message into each embeddable pixel or pattern. To improve the embedding efficiency and payload, we propose an RDH method used in halftone images based on the dynamic embedding states group (DESG), which can embed at least 1 bit of secret messages per embeddable pixel or pattern. First, by exploiting the statistical features of$4 \times 4$patterns and the state sequences in each image, the DESG is constructed dynamically, including$n$embedding states with their state patterns and state sequences. Then, secret messages are encoded by matching the longest common subsequence according to the DESG, which are split into several state sequences. The state sequences are embedded by Markov transitions between these$n$changing state patterns. Finally, reversibility is achieved by recording the DESG as the overhead information in RDH. Experiments show that the construction of DESG can improve the embedding efficiency under the same number of embeddable pixels or patterns, and the visual distortion is also significantly reduced by flipping fewer pixels.

Most state-of-the-art halftone image steganographic techniques only consider the flipping distortion according to the human visual system, which are not always secure when they are attacked by steganalyzers. In this paper, we propose a halftone image steganographic scheme that aims to generate stego images with good visual quality and strong statistical security of anti-steganalysis. First, the concept of pixel density is proposed and a novel construction called pixel density histogram (PDH) is proposed to design a “embedding” scheme for halftone images. Then, we optimize density pair selection to select density blocks that can improve visual quality. Finally, the messages are embedded through pixel density transition, where a novel pixel flipping strategy is proposed, which can maintain the structural dependence by optimizing the pixel mesh Markov transition matrix (PMMTM). The experimental results demonstrate that the proposed steganography scheme can achieve strong statistical security of anti-steganalysis with good visual quality without degrading the embedding capacity.

In this paper, we improve the parallel deep neural network (DNN) scheme BusterNet for image copy-move forgery localization with source/target region distinguishment. BusterNet is based on two branches, i.e., Simi-Det and Mani-Det, and suffers from two main drawbacks: (a) it should ensure that both branches correctly locate regions; (b) the Simi-Det branch only extracts single-level and low-resolution features using VGG16 with four pooling layers. To ensure the identification of the source and target regions, we introduce two subnetworks that are constructed serially: the copy-move similarity detection network (CMSDNet) and the source/target region distinguishment network (STRDNet). Regarding the second drawback, the CMSDNet subnetwork improves Simi-Det by removing the last pooling layer in VGG16 and by introducing atrous convolution into VGG16 to preserve field-of-views of filters after the removal of the fourth pooling layer; double-level self-correlation is also considered for matching hierarchical features. Moreover, atrous spatial pyramid pooling and attention mechanism allow the capture of multiscale features and provide evidence for important information. Finally, STRDNet is designed to determine the similar regions obtained from CMSDNet directly as tampered regions and untampered regions. It determines regions at the image-level rather than at the pixel-level as made by Mani-Det of BusterNet. Experimental results on four publicly available datasets (new synthetic dataset, CASIA, CoMoFoD, and COVERAGE) demonstrate that the proposed algorithm is superior to the state-of-the-art algorithms in terms of similarity detection ability and source/target distinguishment ability.

Due to the popularity of JPEG format images in recent years, JPEG images will inevitably involve image editing operation. Thus, some tramped images will leave tracks of Non-aligned double JPEG ( NA-DJPEG ) compression. By detecting the presence of NA-DJPEG compression, one can verify whether a given JPEG image has been tampered with. However, only few methods can identify NA-DJPEG compressed images in the case that the primary quality factor is greater than the secondary quality factor. To address this challenging task, this article proposes a novel feature extraction scheme based optimized pixel difference ( OPD ), which is a new measure for blocking artifacts. Firstly, three color channels (RGB) of a reconstructed image generated by decompressing a given JPEG color image are mapped into spherical coordinates to calculate amplitude and two angles (azimuth and zenith). Then, 16 histograms of OPD along the horizontal and vertical directions are calculated in the amplitude and two angles, respectively. Finally, a set of features formed by arranging the bin values of these histograms is used for binary classification. Experiments demonstrate the effectiveness of the proposed method, and the results show that it significantly outperforms the existing typical methods in the mentioned task.

Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography. The embedding framework consists of three parts: a U-Net based generator, an embedding simulator, and a discriminator. For practical applications, Syndrome-Trellis Coding (STC) is used to generate stego audio with the learned embedding probability. Experimental results on the UME-ERJ and WSJ speech datasets have shown that the proposed framework can automatically learn the adaptive embedding probabilities for audio steganogra- phy and has a considerable advantage in terms of resisting steganalyzers in comparison with the existing conventional method.

The architectures of convolutional neural networks used in steganalysis have been designed heuristically. In this paper, an automatic Network Architecture Generation algorithm based on reinforcement learning for JPEG image Steganalysis (JS-NAG) has been proposed. Different from the automatic neural network generation methods in computer vision which are based on the strong content signals, steganalysis is based on the weak embedded signals, thus needs specific design. In the proposed method, the agent is trained to sequentially select some high-performing blocks using Q-learning to generate networks. An early stop strategy and a well-designed performance prediction function have been utilized to reduce the search time. To generate the optimal networks, hundreds of networks have been searched and trained on 3 GPUs for 15 days. To further improve the detection accuracy, we make an ensemble classifier out of the generated convolutional neural networks. The experimental results have shown that the proposed method significantly outperforms the current state-of-the-art CNN based methods.

In recent years, with the improvements in machine learning, image forensics has made considerable progress in detecting editing manipulations. This progress also raises more questions in image forensics research, such as can the parameters applied in a manipulation be estimated. Many parameter estimation works have already been performed. However, most of these works are based on mathematical analyses. In this paper, we attempt to solve a particular parameter estimation problem from a different aspect. Specifically, a new convolutional neural network (CNN) model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled. This model features an original layer to generate a measurable energy map toward the estimation of resampling rate (METEOR). The METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN. Furthermore, the METEOR layer can also increase the robustness of the CNN against JPEG compression, which makes it extremely important in realistic application scenarios. Our work has verified that machine learning, particularly CNNs, with proper optimization can also be refined to adapt to parameter estimation in digital forensics with excellent performance and robustness.

Resampling detection is one of the most important topics in image forensics, and the most widely used method in resampling detection is spectral analysis. Since JPEG is the most widely used image format, it is reasonable that the resampling operation is processed on JPEG images. JPEG block artifacts bring severe interference to spectrum-based methods and degrade the detection performance. In addition, the spectral characteristics of the downscaling scenarios are very weak. The detection of downscaling still presents a considerable challenge to forensic applications. In this paper, we propose a method to estimate the downscaling factors of pre-JPEG compressed images in the presence of image downscaling after JPEG compressions. We first analyze the spectrum of scaled images and give an exact formulation of how the scaling factors influence the appearance of periodic artifacts. The expected positions of the characteristic resampling peaks are analytically derived. For the downscaling scenario, the shifted JPEG block artifacts produce periodic peaks, which cause misdetection in the characteristic peak. We find that the interval between the adjacent extrema of difference images obeys the geometric distribution and the distribution has periodic peaks for JPEG images. Hence, we adopt the difference image extremum interval histogram and combine the spectral method to obtain the final estimation. The experimental results demonstrate that the proposed detection method outperforms some state-of-the-art methods.

Utilizing statistical models of binary images is a common and effective means to steganalyze binary images, and the design of the statistical model is essential to the performance of steganalysis. In this paper, we propose a new model based on a histogram of pixel structuring elements (SEs), which is a suitable representation of a binary image for the task of steganalysis. The texture property and the dependency among pixels are considered inside the SEs. The SEs with different patterns will be evaluated comprehensively according to a statistical criterion, and some of them will be selected to construct the feature set for training the steganalyzer. The distributions of these selected SEs, which contain many highly flippable pixels, will be emphasized by the criterion, and they can reflect the difference between cover images and stego-images. Finally, a series of experiments are conducted on two datasets, and the results show that the proposed scheme significantly outperforms state-of-the-art schemes.

Reversible data hiding (RDH) has unique advantage in copyright and integrity protection for multimedia contents. As a typical RDH scheme, histogram shifting technique (HS) has found wide applications due to its high quality of marked image. At present, most existing HS-based RDH schemes rely on single histogram generated from cover image to hide data. Since the single histogram-based approach (SH_RDH) commonly employs smooth regions in the cover image for data hiding, it might not well utilize the cover image and exploit the correlations among image contents of different texture characteristics. In this paper, a novel RDH general framework using multiple histograms modification (MH_RDH) is proposed, which involves two key issues as follows: 1) the construction of multiple histograms based on optimized multi-features and 2) the rate allocation among multiple histograms is formulated as the one of rate-distortion optimization and solved with evolutionary algorithms. The experimental results show that the proposed method could considerably increase the payload of current MH_RDH-based embedding (ranging from 0.2 to 0.7 bpp for most test images) and outperform the other state-of-the-art SH_RDH and MH_RDH schemes.

Detection of double Joint Photographic Experts Group (JPEG) compression is an important part of image forensics. Although methods in the past studies have been presented for detecting the double JPEG compression with a different quantization matrix, the detection of double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective method to detect the recompression in the color images by using the conversion error, rounding error, and truncation error on the pixel in the spherical coordinate system is proposed. The randomness of truncation errors, rounding errors, and quantization errors result in random conversion errors. The pixel number of the conversion error is used to extract six-dimensional features. Truncation error and rounding error on the pixel in its three channels are mapped to the spherical coordinate system based on the relation of a color image to the pixel values in the three channels. The former is converted into amplitude and angles to extract 30-dimensional features and 8-dimensional auxiliary features are extracted from the number of special points and special blocks. As a result, a total of 44-dimensional features have been used in the classification by using the support vector machine (SVM) method. Thereafter, the support vector machine recursive feature elimination (SVMRFE) method is used to improve the classification accuracy. The experimental results show that the performance of the proposed method is better than the existing methods.

Due to their good rotational invariance and stability, image continuous orthogonal moments are intensively applied in rotationally invariant recognition and image processing. However, most moments produce numerical instability, which impacts the image reconstruction and recognition performance. In this paper, a new set of invariant continuous orthogonal moments, polar harmonic Fourier moments (PHFMs), free of numerical instability is designed. The radial basis functions (RBFs) of the PHFMs are much simpler than those of the Chebyshev-Fourier moments (CHFMs), orthogonal Fourier-Mellin moments (OFMMs), Zernike moments (ZMs), and pseudo-Zernike moments (PZMs). For the same degree, the RBFs of the PHFMs have more zeros and are more evenly distributed than those of the ZMs and PZMs. Therefore, PHFMs do not suffer from information suppression problem; hence, the image description ability of the PHFMs is superior to that of the ZMs and PZMs. Moreover, the RBFs of the PHFMs are always less than or equal to 1.0 near the unit disk center, whereas those of the OFMMs, PZMs, CHFMs, and radial harmonic Fourier moments (RHFMs) are infinite (implying numerical instability). This indicates that PHFMs can outperform these moments in image reconstruction tasks. We theoretically and experimentally demonstrate that PHFMs outperform the above moments in reconstructing images and recognizing rotationally invariant objects considering noise and various attacks. This paper also details the significance of the PHFM phase in image reconstruction, angle estimation using PHFMs, and the accurate moment selection of the PHFMs.

In this paper, a binary image steganographic scheme is presented, which aims at minimizing the embedding distortions measured by prediction. A prediction model of the center pixel's value is established in a 3 × 3 local region. A concept of “uncertainty” is introduced to represent the prediction result and the uncertainty is defined as the proximity of probabilities about whether the center pixel is black or white. A pixel with high uncertainty means that it is hard to distinguish whether it has been flipped or not, and thus the distortion introduced by flipping this pixel is small. The uncertainty is an appended statistical explanation of human visual perception and the distortion measurement based on it can evaluate the embedding changes on both vision and statistics. Benefiting from the statistics, uncertainty can evaluate the distortion influence in an extended local region. To play the advantage of distortion measurement, the syndrome-trellis code (STC) is employed to minimize the embedding distortions. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high vision imperceptibility and statistical security.

Successful adaptive steganography has mainly focused on embedding the payload while minimizing an appropriately defined distortion function. The application of deep learning to steganalysis has greatly challenged present adaptive steganographic methods, but has also shown the potential for the improvement of steganography. This paper proposes a distortion function generating a framework for steganography. It has three modules: a generator with a U-Net architecture to translate a cover image into an embedding change probability map, a no-pre-training-required double-tanh function to approximate the optimal embedding simulator while preserving gradient norm during backpropagation in the adversarial training, and an enhanced steganalyzer based on a convolution neural network together with multiple high pass filters as the discriminator. Extensive experimental results on different datasets have shown that the proposed framework outperforms the current state-of-the-art steganographic schemes. Moreover, the adversarial training time is reduced dramatically compared with the GAN-based automatic steganographic distortion learning framework (ASDL-GAN).

In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.

Current mainstream methods for digital image steganography are content adaptive. That is, the secret messages are embedded in the complicated region in the cover image while minimizing the embedding distortion so as to suppress statistical detectability. Since there is already a practical encoding scheme for data embedding near the payload-distortion bound, the design of the embedding cost function becomes a deterministic part in steganography. Unlike the traditional heuristic hand-crafted method, this paper proposes a novel generative adversarial network based framework to automatically learn the embedding cost function for JPEG steganography. The proposed framework consists of a generator, a gradient-descent friendly inverse discrete cosine transformation module, an embedding simulator and a discriminator for steganalysis. Through training the generator and discriminator in alternation, the embedding cost function can finally be obtained by the trained generator. Experimental results demonstrate that our method can automatically learn a reasonable embedding cost function and achieve a satisfying performance.

In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.

In this paper, a novel forensics scheme for color image is proposed in color quaternion wavelet transform (CQWT) domain. Compared with discrete wavelet transform (DWT), contourlet wavelet transform, and local binary patterns, CQWT processes a color image as a unit, and so, it can provide more forensics information to identify the photograph (PG) and computer generated (CG) images by considering the quaternion magnitude and phase measures. Meanwhile, two novel quaternion central moments for color images, i.e., quaternion skewness and kurtosis, are proposed to extract forensics features. In the condition of the same statistical model as Farid's model, the CQWT can boost the performance of the existing identification models. Compared with Farid's model and Li's model in 7500 PG and 7500 CG, the quaternion statistical features show a better classification performance. Results in the comparative experiments show that the classification accuracy of the CQWT improves by 19% more than Farid's model, and the quaternion features approximately improve by 2% more than the traditional.

Nowadays, JPEG steganographic schemes, e.g., J-UNIWARD, which take into account the effects of embedding in the spatial domain tend to exhibit higher security and introduce less artifacts that can be captured by the prevalent steganalyzers. Following the paradigm, this letter proposes a new design of the distortion measure for JPEG steganography by incorporating the statistics of both the spatial and discrete cosine transform (DCT) domains. The spatial statistics of the decompressed JPEG images are first well characterized with distortion measures of some efficient steganographic schemes in the spatial domain, e.g., HILL, and the resulting embedding entropies of spatial blocks in alignment with DCT blocks are then transformed into the DCT domain to obtain the distortion measures for JPEG steganography. Experimental results show that the proposed method outperforms considerably other state-of-the-art JPEG steganographic schemes, i.e., J-UNIWARD and UERD, for the most effective feature set GFR at present, and rivals them for other feature sets, e.g., DCTR and CC-JRM.

Nowadays, the most prevailing approach to steganography is the minimal embedding distortion framework, which includes an optimizable distortion function for each cover element and an encoding method to minimize the distortion. With the emergence of Syndrome-Trellis Code, the distortion function plays an increasingly important role in modern adaptive image steganography. In this letter, a new distortion function called generalized uniform embedding distortion (GUED) is proposed for JPEG steganography. The proposed GUED consists of the new distortion measures for both Alternating Current (AC) mode and Discrete Cosine Transform (DCT) block, which are represented in a more general exponential model, aiming to flexibly allocate the embedding data so as to minimize the global changes of the statistics of quantized DCT coefficients after embedding. In addition, an empirical rule is developed to determine the parameters of the exponential function according to the payload and quality factor. By exploring the statistics of both DCT and spatial domains, the proposed GUED is shown to be more consistent with the objective of generalized uniform embedding strategy, i.e., maintaining the relative changes of DCT coefficients to be proportional to their coefficients of variations. Extensive experiments demonstrate that the proposed GUED gains significant performance improvements when compared with its original UERD, and outperforms the state-of-the-art J-UNIWARD with markedly reduced computation time.

Security analysis is a very important issue for digital watermarking. Several years ago, according to Kerckhoffs' principle, the famous four security levels, namely insecurity, key security, subspace security, and stego-security, were defined for spread-spectrum (SS) embedding schemes in the framework of watermarked-only attack. However, up to now there has been little application of the definition of these security levels to the theoretical analysis of the security of SS embedding schemes, due to the difficulty of the theoretical analysis. In this paper, based on the security definition, we present a theoretical analysis to evaluate the security levels of five typical SS embedding schemes, which are the classical SS, the improved SS (ISS), the circular extension of ISS, the nonrobust and robust natural watermarking, respectively. The theoretical analysis of these typical SS schemes are successfully performed by taking advantage of the convolution of probability distributions to derive the probabilistic models of watermarked signals. Moreover, simulations are conducted to illustrate and validate our theoretical analysis. We believe that the theoretical and practical analysis presented in this paper can bridge the gap between the definition of the four security levels and its application to the theoretical analysis of SS embedding schemes.

During the past twenty years, there has been a great interest in the study of spread spectrum (SS) watermarking. However, it is still a challenging task to design a secure and robust SS watermarking method. In this paper, we first define a family of secure SS watermarking methods, named as spherical watermarking (SW). The watermarked correlation of SW is defined to be uniformly distributed on a spherical surface, and this makes SW be key-secure against the watermarked-only attack. Then, we propose an implementation of SW, called transportation SW (TSW), which is designed to decrease embedding distortion in a recursive manner using the transportation theory, meanwhile keeping the security of SW. Moreover, we present a theoretical analysis of the embedding distortion and robustness of the proposed method. Finally, extensive experiments are conducted on simulated signals and real images. The experimental results show that TSW is more robust than existing secure SS watermarking methods.

Multiple‐compression detection is of particular importance in video forensics, as it reveals possible manipulations to the content. However, methods for detecting multiple compressions with same quantisation parameters (QPs) are rarely reported. To deal with this issue, a novel method is presented in this study to detect multiple H.264/advanced video coding compressions with the same QPs. First, a new set, named ratio difference set (RDS), is proposed, which is calculated by identifying the quantised DCT coefficients whose values will be changed after re‐compression. Then, a discriminative and fixed statistical feature set extracted from RDS of each video is obtained to serve as input for classification. With the aid of support vector machines, the extracted feature set is used to classify the videos that have undergone H.264 compressions twice or more from those compressed just once. Experimental results show that high classification accuracy and robustness against copy‐move attack and frame‐deletion attack can be achieved with the authors’ proposed method.

Reversible data hiding (RDH) into encrypted images is of increasing attention to researchers as the original content can be perfectly reconstructed after the embedded data are extracted while the content owner's privacy remains protected. The existing RDH techniques are designed for grayscale images and, therefore, cannot be directly applied to palette images. Since the pixel values in a palette image are not the actual color values, but rather the color indexes, RDH in encrypted palette images is more challenging than that designed for normal image formats. To the best knowledge of the authors, there is no suitable RDH scheme designed for encrypted palette images that has been reported, while palette images have been widely utilized. This has motivated us to design a reliable RDH scheme for encrypted palette images. The proposed method adopts a color partitioning method to use the palette colors to construct a certain number of embeddable color triples, whose indexes are self-embedded into the encrypted image so that a data hider can collect the usable color triples to embed the secret data. For a receiver, the embedded color triples can be determined by verifying a self-embedded check code that enables the receiver to retrieve the embedded data only with the data hiding key. Using the encryption key, the receiver can roughly reconstruct the image content. Experiments have shown that our proposed method has the property that the presented data extraction and image recovery are separable and reversible. Compared with the state-of-the-art works, our proposed method can provide a relatively high data-embedding payload, maintain high peak signal-to-noise ratio values of the decrypted and marked images, and have a low computational complexity.

Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.

Image forensics with the presence of an adversary, such as the interplay between the sensor-based camera source identification (CSI) and the fingerprint-copy attack, has attracted increasing attention recently. In this paper, we propose a framework of CSI game with both complete information and incomplete information. A noise level-based counter anti-forensic method is presented to detect the potential fingerprint-copy attack, and unlike the state-of-the-art countermeasure of the triangle test, it does not need to collect the candidate image set. With the existence of countermeasure, a rational forger needs to balance the tradeoff between synthesizing source information and leaving new detectable evidence of raising the noise level of a forged image. The mixed-strategy other than the sequential-move assumption is adopted to solve the games. The Bayesian game is introduced to address the information asymmetry in practice. The Nash equilibrium of both the complete information game and Bayesian game are theoretically analyzed, and the expected Nash equilibrium payoff of a Bayesian game is obtained. Nash equilibrium receiver operating characteristic curves are adopted to evaluate the detection performance. Simulation results show that the information asymmetry can remarkably affect the final detection performance. To our knowledge, this paper is the first attempt in analyzing a Bayesian forensic game with practical information asymmetry.

Geometric transformations, such as resizing and rotation, are almost always needed when two or more images are spliced together to create convincing image forgeries. In recent years, researchers have developed many digital forensic techniques to identify these operations. Most previous works in this area focus on the analysis of images that have undergone single geometric transformations, e.g., resizing or rotation. In several recent works, researchers have addressed yet another practical and realistic situation: successive geometric transformations, e.g., repeated resizing, resizing-rotation, rotation-resizing, and repeated rotation. We will also concentrate on this topic in this paper. Specifically, we present an in-depth analysis in the frequency domain of the second-order statistics of the geometrically transformed images. We give an exact formulation of how the parameters of the first and second geometric transformations influence the appearance of periodic artifacts. The expected positions of characteristic resampling peaks are analytically derived. The theory developed here helps to address the gap left by previous works on this topic and is useful for image security and authentication, in particular, the forensics of geometric transformations in digital images. As an application of the developed theory, we present an effective method that allows one to distinguish between the aforementioned four different processing chains. The proposed method can further estimate all the geometric transformation parameters. This may provide useful clues for image forgery detection.

In this paper, based on the principle of Code Division Multiple Access (CDMA), a novel reversible data hiding scheme is presented. The to-be-embedded data are represented by different orthogonal spreading sequences and embedded into a cover image while degrading the image quality slightly. According to the feature of orthogonality, different spreading sequences are repeatedly embedded into the image without disturbing each other, and most elements of different spreading sequences are mutually cancelled in the process of multilevel data embedding. Thus, it keeps the distortion of the embedded image at a relatively low level even with a high embedding capacity. Moreover, the location-map of the proposed scheme can be highly compressed and thus the size is quite small; it further helps to obtain high net embedding capacity. Experimental results have demonstrated that the CDMA based reversible data hiding scheme can achieve higher image quality at the moderate-to-high embedding capacity than other state-of-the-art reversible data hiding works.

We propose to utilize the prediction-error of prediction error (PPE) of a pixel to reversibly carry the secret data in this letter. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the prediction errors (PEs). By exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels to be embedded can be then determined. And, a sorting technique based on the local complexity of a pixel is used to collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will be processed first for data embedding. By reversibly shifting the PPE histogram (PPEH) with optimized parameters, the pixels corresponding to the altered PPEH bins can be finally modified to carry the entire secret data. Experimental results have implied that, the proposed algorithm can benefit from the prediction procedure, sorting technique as well as parameters selection, and therefore outperform some state-of-the-art works in terms of payload-distortion performance.

There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually, a single CNN model is trained and then tested in experiments. It is known that, neural networks, including CNNs, are suitable to form ensembles. From this perspective, in this paper, we employ CNNs as base learners and test several different ensemble strategies. In our study, at first, a recently proposed CNN architecture is adopted to build a group of CNNs, each of them is trained on a random subsample of the training dataset. The output probabilities, or some intermediate feature representations, of each CNN, are then extracted from the original data and pooled together to form new features ready for the second level of classification. To make best use of the trained CNN models, we manage to partially recover the lost information due to spatial subsampling in the pooling layers when forming feature vectors. Performance of the ensemble methods are evaluated on BOSSbase by detecting S-UNIWARD at 0.4 bpp embedding rate. Results have indicated that both the recovery of the lost information, and learning from intermediate representation in CNNs instead of output probabilities, have led to performance improvement.

Digital audio watermarking is an important technique to secure and authenticate audio media. This paper provides a comprehensive review of the twenty years’ research and development works for digital audio watermarking, based on an exhaustive literature survey and careful selections of representative solutions. We generally classify the existing designs into time domain and transform domain methods , and relate all the reviewed works using two generic watermark embedding equations in the two domains. The most important designing criteria, i.e., imperceptibility and robustness, are thoroughly reviewed. For imperceptibility , the existing measurement and control approaches are classified into heuristic and analytical types, followed by intensive analysis and discussions. Then, we investigate the robustness of the existing solutions against a wide range of critical attacks categorized into basic, desynchronization, and replacement attacks, respectively. This reveals current challenges in developing a global solution robust against all the attacks considered in this paper. Some remaining problems as well as research potentials for better system designs are also discussed. In addition, audio watermarking applications in terms of US patents and commercialized solutions are reviewed. This paper serves as a comprehensive tutorial for interested readers to gain a historical, technical, and also commercial view of digital audio watermarking.

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.

In the past more than one decade, hundreds of reversible data hiding (RDH) algorithms have been reported. Via exploring the correlation between the neighboring pixels (or coefficients), extra information can be embedded into the host image reversibly. However, these RDH algorithms cannot be accomplished in encrypted domain directly, since the correlation between the neighboring pixels will disappear after encryption. In order to accomplish RDH in encrypted domain, specific RDH schemes have been designed according to the encryption algorithm utilized. In this paper, we propose a new simple yet effective framework for RDH in encrypted domain. In the proposed framework, the pixels in a plain image are first divided into sub-blocks with the size of $m\times n$ . Then, with an encryption key, a key stream (a stream of random or pseudorandom bits/bytes that are combined with a plaintext message to produce the encrypted message) is generated, and the pixels in the same sub-block are encrypted with the same key stream byte. After the stream encryption, the encrypted $m\times n$ sub-blocks are randomly permutated with a permutation key. Since the correlation between the neighboring pixels in each sub-block can be well preserved in the encrypted domain, most of those previously proposed RDH schemes can be applied to the encrypted image directly. One of the main merits of the proposed framework is that the RDH scheme is independent of the image encryption algorithm. That is, the server manager (or channel administrator) does not need to design a new RDH scheme according to the encryption algorithm that has been conducted by the content owner; instead, he/she can accomplish the data hiding by applying the numerous RDH algorithms previously proposed to the encrypted domain directly.

In this paper, a novel code division multiplexing (CDM) algorithm-based reversible data hiding (RDH) scheme is presented. The covert data are denoted by different orthogonal spreading sequences and embedded into the cover image. The original image can be completely recovered after the data have been extracted exactly. The Walsh Hadamard matrix is employed to generate orthogonal spreading sequences, by which the data can be overlappingly embedded without interfering each other, and multilevel data embedding can be utilized to enlarge the embedding capacity. Furthermore, most elements of different spreading sequences are mutually cancelled when they are overlappingly embedded, which maintains the image in good quality even with a high embedding payload. A location-map free method is presented in this paper to save more space for data embedding, and the overflow/underflow problem is solved by shrinking the distribution of the image histogram on both the ends. This would further improve the embedding performance. Experimental results have demonstrated that the CDM-based RDH scheme can achieve the best performance at the moderate-to-high embedding capacity compared with other state-of-the-art schemes.

The local variance of image intensity is a typical measure of image smoothness. It has been extensively used, for example, to measure the visual saliency or to adjust the filtering strength in image processing and analysis. However, to the best of our knowledge, no analytical work has been reported about the effect of JPEG compression on image local variance. In this paper, a theoretical analysis on the variation of local variance caused by JPEG compression is presented. First, the expectation of intensity variance of 8×8 non-overlapping blocks in a JPEG image is derived. The expectation is determined by the Laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the JPEG compression. Second, some interesting properties that describe the behavior of the local variance under different degrees of JPEG compression are discussed. Finally, both the simulation and the experiments are performed to verify our derivation and discussion. The theoretical analysis presented in this paper provides some new insights into the behavior of local variance under JPEG compression. Moreover, it has the potential to be used in some areas of image processing and analysis, such as image enhancement, image quality assessment, and image filtering.

Unsharp masking (USM) sharpening is a basic technique for image manipulation and editing. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. After USM sharpening, overshoot artifacts, which shape image texture, are generated along image edges. By utilizing the special characteristic of the texture modification caused by the USM sharpening, a novel method called edge perpendicular binary coding is proposed in this letter to detect USM sharpening. Extensive experiments have been conducted to show the superiority of the proposed method over the existing methods.

The conventional reversible data hiding (RDH) algorithms pursue high Peak-Signal-to-Noise-Ratio (PSNR) at the certain amount of embedding bits. Recently, Wu et al. deemed that the improvement of image visual quality is more important than keeping high PSNR. Based on this viewpoint, they presented a novel RDH scheme, utilizing contrast enhancement to replace the PSNR. However, when a large number of bits are embedded, image contrast is over-enhanced, which introduces obvious distortion for human visual perception. Motivated by this issue, a new RDH scheme is proposed using the controlled contrast enhancement (CCE) and Haar integer wavelet transform (IWT). The proposed scheme has large embedding capacity while maintaining satisfactory visual perception. Experimental results have demonstrated the effectiveness of the proposed scheme.

In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of trying to keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visual quality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can be performed by repeating the process. The side information is embedded along with the message bits into the host image so that the original image is completely recoverable. The proposed algorithm was implemented on two sets of images to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrast enhancement by RDH. Furthermore, the evaluation results show that the visual quality can be preserved after a considerable amount of message bits have been embedded into the contrast-enhanced images, even better than three specific MATLAB functions used for image contrast enhancement.

Uniform embedding was first introduced in 2012 for non-side-informed JPEG steganography, and then extended to the side-informed JPEG steganography in 2014. The idea behind uniform embedding is that, by uniformly spreading the embedding modifications to the quantized discrete cosine transform (DCT) coefficients of all possible magnitudes, the average changes of the first-order and the second-order statistics can be possibly minimized, which leads to less statistical detectability. The purpose of this paper is to refine the uniform embedding by considering the relative changes of statistical model for digital images, aiming to make the embedding modifications to be proportional to the coefficient of variation. Such a new strategy can be regarded as generalized uniform embedding in substantial sense. Compared with the original uniform embedding distortion (UED), the proposed method uses all the DCT coefficients (including the DC, zero, and non-zero AC coefficients) as the cover elements. We call the corresponding distortion function uniform embedding revisited distortion (UERD), which incorporates the complexities of both the DCT block and the DCT mode of each DCT coefficient (i.e., selection channel), and can be directly derived from the DCT domain. The effectiveness of the proposed scheme is verified with the evidence obtained from the exhaustive experiments using a popular steganalyzer with rich models on the BOSSbase database. The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.

ABSTRACT With the ongoing development of rendering technology, computer graphics (CG) are sometimes so photorealistic that to distinguish them from photographic (PG) images by human eyes has become difficult. To this end, many methods have been developed for automatic CG and PG classification. In this paper, we present a simple, yet efficient, multiresolution approach to distinguish CG from PG based on uniform gray‐scale invariant local binary patterns (LBPs) with the help of support vector machines (SVM). We select YCbCr as the color model. The original Joint Photographic Experts Group (JPEG) coefficients of Y, Cb, and Cr components and their prediction errors are used for two LBP operators. From each 2D array and each LBP operator, we obtain 59 uniform LBP features. In total, 12 groups of 59 features are obtained from each image. But after multiresolution analysis, we select six groups of 59 features for CG and PG classification. The proposed features have been tested with thousands of CG and PG. Classification accuracy reaches 95.1% with support vector machines and outperforms the state‐of‐the‐art works. Copyright © 2013 John Wiley & Sons, Ltd.

Recently, detecting the traces introduced by the content-preserving image manipulations has received a great deal of attention from forensic analyzers. It is well known that the median filter is a widely used nonlinear denoising operator. Therefore, the detection of median filtering is of important realistic significance in image forensics. In this letter, a novel local texture operator, named the second-order local ternary pattern (LTP), is proposed for median filtering detection. The proposed local texture operator encodes the local derivative direction variations by using a 3-valued coding function and is capable of effectively capturing the changes of local texture caused by median filtering. In addition, kernel principal component analysis (KPCA) is exploited to reduce the dimensionality of the proposed feature set, making the computational cost manageable. The experiment results have shown that the proposed scheme performs better than several state-of-the-art approaches investigated.

Steganography is the science and art of covert communication, which aims to hide the secret messages into a cover medium while achieving the least possible statistical detectability. To this end, the framework of minimal distortion embedding is widely adopted in the development of the steganographic system, in which a well designed distortion function is of vital importance. In this paper, a class of new distortion functions known as uniform embedding distortion function (UED) is presented for both side-informed and non side-informed secure JPEG steganography. By incorporating the syndrome trellis coding, the best codeword with minimal distortion for a given message is determined with UED, which, instead of random modification, tries to spread the embedding modification uniformly to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this way, less statistical detectability is achieved, owing to the reduction of the average changes of the first- and second-order statistics for DCT coefficients as a whole. The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis.

Digital video sometimes needs to be stored and processed in an encrypted format to maintain security and privacy. For the purpose of content notation and/or tampering detection, it is necessary to perform data hiding in these encrypted videos. In this way, data hiding in encrypted domain without decryption preserves the confidentiality of the content. In addition, it is more efficient without decryption followed by data hiding and re-encryption. In this paper, a novel scheme of data hiding directly in the encrypted version of H.264/AVC video stream is proposed, which includes the following three parts, i.e., H.264/AVC video encryption, data embedding, and data extraction. By analyzing the property of H.264/AVC codec, the codewords of intraprediction modes, the codewords of motion vector differences, and the codewords of residual coefficients are encrypted with stream ciphers. Then, a data hider may embed additional data in the encrypted domain by using codeword substitution technique, without knowing the original video content. In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain. Furthermore, video file size is strictly preserved even after encryption and data embedding. Experimental results have demonstrated the feasibility and efficiency of the proposed scheme.

Detection of double JPEG compression plays an important role in digital image forensics. Some successful approaches have been proposed to detect double JPEG compression when the primary and secondary compressions have different quantization matrices. However, detecting double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective error-based statistical feature extraction scheme is presented to solve this problem. First, a given JPEG file is decompressed to form a reconstructed image. An error image is obtained by computing the differences between the inverse discrete cosine transform coefficients and pixel values in the reconstructed image. Two classes of blocks in the error image, namely, rounding error block and truncation error block, are analyzed. Then, a set of features is proposed to characterize the statistical differences of the error blocks between single and double JPEG compressions. Finally, the support vector machine classifier is employed to identify whether a given JPEG image is doubly compressed or not. Experimental results on three image databases with various quality factors have demonstrated that the proposed method can significantly outperform the state-of-the-art method.

In this paper, we present a new framework for designing distortion functions of joint photographic experts group (JPEG) steganography with uncompressed side-image. In our framework, the discrete cosine transform (DCT) coefficients, including all direct current (DC) coefficients and alternating current (AC) coefficients, are divided into two groups: first-priority group (FPG) and second-priority group (SPG). Different strategies are established to associate the distortion values to the coefficients in FPG and SPG, respectively. In this paper, three scenarios for dividing the coefficients into FPG and SPG are exemplified, which can be utilized to form a series of new distortion functions. Experimental results demonstrate that while applying these generated distortion functions to JPEG steganography, the intrinsic statistical characteristics of the carrier image will be preserved better than the prior-art, and consequently the security performance of the corresponding JPEG steganography can be improved significantly.

With the spread of powerful and easy-to-use video editing software, digital videos are exposed to various forms of tampering. Nowadays, a considerable proportion of surveillance systems and video cameras have built-in MPEG-4 codec. Therefore, the detection of double compression in MPEG-4 videos as a first step in video forensics research is of significance. In this paper, Markov based features are adopted to detect double compression artifacts, which imply that the original video may have been interpolated. The advantages and limitations of double MPEG-4 compression detection are analyzed. Experimental results have demonstrated that our scheme outperforms most existing methods.

Covert channels via the widely used TCP/IP protocols have become a new challenging issue for network security. In this paper, we analyze the information hiding in TCP/IP protocols and propose a new effective method to detect the existence of hidden information in TCP initial sequence numbers (ISNs), which is known as one of the most difficult covert channels to be detected. Our method uses phase space reconstruction to create a processing space called reconstructed phase space, where a statistical model is proposed for detecting covert channels in TCP ISNs. Based on the model, a classification algorithm is developed to identify the existence of information hidden in ISNs. Simulation results have demonstrated that our proposed detection method outperforms the state-of-the-art technique in terms of high detection accuracy and greatly reduced computational complexity. Instead of offline processing as the state-of-the-art does, our new scheme can be used for online detection.

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.

In digital image forensics, camera model identification seeks for the source camera model information from the given images under investigation. To achieve this goal, one of the popular approaches is extracting from the images under investigation certain statistical features that capture the difference caused by camera structure and various in-camera image processing algorithms, followed by machine learning and pattern recognition algorithms for similarity measures of extracted features. In this paper, we propose to use uniform gray-scale invariant local binary patterns (LBP) as statistical features. Considering 8-neighbor binary co-occurrence, three groups of 59 local binary patterns are extracted from the spatial domain of red and green color channels, their corresponding prediction-error arrays, and their 1st-level diagonal wavelet sub bands of each image, respectively. Multi-class support vector machines are built for classification of 18 camera models from 'Dresden Image Database'. Compared with the results reported in literatures, the detection accuracy reported in this paper is higher.

In this paper, we present a new channel selection rule for joint photographic experts group (JPEG) steganography, which can be utilized to find the discrete cosine transform (DCT) coefficients that may introduce minimal detectable distortion for data hiding. Three factors are considered in our proposed channel selection rule, i.e., the perturbation error (PE), the quantization step (QS), and the magnitude of quantized DCT coefficient to be modified (MQ). Experimental results demonstrate that higher security performance can be obtained in JPEG steganography via our new channel selection rule.

Video watermarking has received much attention over the past years as a promising solution to copy protection. Watermark robustness is still a key issue of research, especially when a watermark is embedded in the compressed video domain. In this article, a robust watermarking scheme for H.264 video is proposed. During video encoding, the watermark is embedded in the index of the reference frame, referred to as reference index, a bitstream syntax element newly proposed in the H.264 standard. Furthermore, the video content (current coded blocks) is modified based on an optimization model, aiming at improving watermark robustness without unacceptably degrading the video's visual quality or increasing the video's bit rate. Compared with the existing schemes, our method has the following three advantages: (1) The bit rate of the watermarked video is adjustable; (2) the robustness against common video operations can be achieved; (3) the watermark embedding and extraction are simple. Extensive experiments have verified the good performance of the proposed watermarking scheme.

In this paper, an effective framework for passive-blind color image forgery detection is proposed. It is a combination of image features extracted from image luminance by applying a rake-transform and from image chroma by using edge statistics. The efficacy of the image features has been tested over two color image datasets established for tampering detection. The proposed framework outweighs the state of the arts over the small-scale dataset, and performs well on the newly established large-scaled dataset (likely the first reported test result on this dataset). The initial tests on some real image forgery cases available in the website and those reported in the literature on image composition with advanced image and vision technologies indicate the promise possessed as well as the challenge faced by the community of image forgery detection.

Randomly selecting 8 × 8 host blocks in big-blocks for data embedding, YASS, a recently developed advanced stegano-graphic scheme makes these blocks not coincident with the 8×8 grids used in JPEG compression. As a result, it effectively invalidates the self-calibration technique used in modern steganaly-sis. However, the randomization is not sufficient enough, i.e., some positions in an image are possible to hold host blocks and some are definitely not. Based on this observation, the newly developed specific steganalyzer can effectively defeat YASS. In this paper, a new steganographic scheme is presented. Through randomizing the size and position of each big-block, our improved steganographic method makes almost every position possible to hold a host block, which has been verified by our statistical analysis. Consequently, the proposed scheme can survive the attack made by the specific steganalyzer. Experimental results have demonstrated that the detection rate achieved by the specific steganalyzer on our proposed method is less than 58%, while that on YASS is about 95% and above.

Face recognition is an increasingly popular method for user authentication. However, face recognition is susceptible to playback attacks. Therefore, a reliable way to detect malicious attacks is crucial to the robustness of the system. We propose and validate a novel physics-based method to detect images recaptured from printed material using only a single image. Micro-textures present in printed paper manifest themselves in the specular component of the image. Features extracted from this component allows a linear SVM classifier to achieve 2.2% False Acceptance Rate and 13% False Rejection Rate (6.7% Equal Error Rate). We also show that the classifier can be generalizable to contrast enhanced recaptured images and LCD screen recaptured images without re-training, demonstrating the robustness of our approach.

In this paper, an efficient framework for passive-blind color image tampering detection is presented. Statistical features are extracted from a given test image and a set of 2-D arrays derived by applying multi-size block discrete cosine transform to the given test image. Image features are extracted from Cr channel, a chroma channel in YCbCr color space, because of its observed sensitivity to color image tampering. A support vector machine is employed to evaluate the effectiveness of image features over a color image dataset recently established for tampering detection. Boosting feature selection is applied to having feature dimensionality reduced so as to make detection accuracy generalizable and computational complexity decreased. Experimental results have demonstrated that the proposed framework applied to the aforementioned dataset outperforms the state of the arts by distinct margins.

This proposed scheme reversibly embeds data into image prediction-errors by using histogram-pair method with double thresholds (embedding threshold and fluctuation threshold). The embedding threshold is used to select only those prediction-errors, whose magnitude does not exceed this threshold, for possible reversible data hiding. The fluctuation threshold is used to select only those prediction-errors, whose associated neighbor fluctuation does not exceed this threshold, for possible reversible data hiding. Only when both thresholds are satisfied the reversible data hiding is carried out. Image gray level histogram modification is conducted to shrink the image histogram towards the center to avoid underflow and/or overflow only when this is necessary. The required bookkeeping data are embedded together with pure payload for original image recovery late. The experimental results have demonstrated that the proposed scheme outperforms recently published reversible image data hiding schemes in terms of the highest PSNR of marked image vs. original image at given pure payloads.

This paper presents an experimental study on the security performance of Yet Another Steganographic Scheme (YASS). It reports: 1) YASS's security performance with different input images, i.e., uncompressed images and JPEG compressed images; 2) YASS's security performance compared with two other JPEG steganographic schemes MB1 and F5; and 3) some experimental results about extended YASS.

Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different quantization matrixes. However, when the primary compression and the secondary compression have the same quantization matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same quantization matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a “proper” randomly perturbed ratio. For different images, this universal “proper” ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.

In this paper, a novel technique is proposed to identify computer graphics by employing second-order statistics to capture the significant statistical difference between computer graphics and photographic images. Due to the wide availability of JPEG images, a JPEG 2-D array formed from the magnitudes of quantized block DCT coefficients is deemed a feasible input; however, a difference JPEG 2-D array tells a better story about image statistics with less influence from image content. Characterized by transition probability matrix (TPM), Markov process, widely used in digital image processing, is applied to model the difference JPEG 2-D arrays along horizontal and vertical directions. We resort to a thresholding technique to reduce the dimensionality of feature vectors formed from TPM. YCbCr color system is selected because of its demonstrated better performance in computer graphics classification than RGB color system. Furthermore, only Y and Cb components are utilized for feature generation because of the high correlation found in the features derived from Cb and Cr components. Finally, boosting feature selection technique is used to greatly reduce the dimensionality of features without sacrificing the machine learning based classification performance.

Camera brand and model identification has become one important task of image forensics. Most of the research on this topic focuses on only one or two parts of camera inner structure. In this paper, we propose a universal image statistical model which takes the whole image formation pipeline of cameras into consideration. By examining their comprehensive effects on the formulated images, our assumption is that any difference of the parts of the image formation pipeline can result in the statistical difference of the output image. Moments of 1-D characteristic functions generated from the given image, its JPEG 2-D array, their prediction-error 2-D arrays, and all of their three-level wavelet subbands, and moments of 2-D characteristic functions generated only from JPEG 2-D array accordingly are used to build the statistical model for classification. Our experimental works have verified the effectiveness of this proposed method.

This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

A promising steganographic method-yet another steganography scheme (YASS)-was designed to resist blind steganalysis via embedding data in randomized locations. In addition to a concrete realization which is named the YASS algorithm in this paper, a few strategies were proposed to work with the YASS algorithm in order to enhance the data embedding rate and security. In this work, the YASS algorithm and these strategies, together referred to as YASS, have been analyzed from a warden's perspective. It is observed that the embedding locations chosen by YASS are not randomized enough and the YASS embedding scheme causes detectable artifacts. We present a steganalytic method to attack the YASS algorithm, which is facilitated by a specifically selected steganalytic observation domain (SO-domain), a term to define the domain from which steganalytic features are extracted. The proposed SO-domain is not exactly, but partially accesses, the domain where the YASS algorithm embeds data. Statistical features generated from the SO-domain have demonstrated high effectiveness in detecting the YASS algorithm and identifying some embedding parameters. In addition, we discuss how to defeat the above-mentioned strategies of YASS and demonstrate a countermeasure to a new case in which the randomness of the embedding locations is enhanced. The success of detecting YASS by the proposed method indicates a properly selected SO-domain is beneficial for steganalysis and confirms that the embedding locations are of great importance in designing a secure steganographic scheme.

YASS (Yet another steganographic scheme) is a newly developed JPEG steganographic method. Through embedding data in the randomized 8×8 blocks which do not coincide with the 8×8 grid used in JPEG compression, it effectively disables the self-calibration process popularly used in today’s JPEG steganalyzers. However, with YASS’ complicated embedding procedure, the intra- and inter-block dependency among the quantized DCT coefficients belonging to the original image is disturbed after the secret message embedding. Furthermore, because of the randomly selection of an 8×8 block within a large block and the necessary utilization of error correction code, the amount of information that YASS can embed is largely reduced. In this paper a study on security performance of YASS is reported. Our experimental results have demonstrated that 1) the steganalyzers which utilizes intra- and/or inter-block correlation of JPEG coefficients can break YASS, 2) with embedding the same amount of information bits, the security of YASS is not stronger than that of MB1 when some today’s blind JPEG steganalyzers are used.

Double JPEG compression detection is of significance in digital forensics. We propose an effective machine learning based scheme to distinguish between double and single JPEG compressed images. Firstly, difference JPEG 2D arrays, i.e., the difference between the magnitude of JPEG coefficient 2D array of a given JPEG image and its shifted versions along various directions, are used to enhance double JPEG compression artifacts. Markov random process is then applied to modeling difference 2-D arrays so as to utilize the second-order statistics. In addition, a thresholding technique is used to reduce the size of the transition probability matrices, which characterize the Markov random processes. All elements of these matrices are collected as features for double JPEG compression detection. The support vector machine is employed as the classifier. Experiments have demonstrated that our proposed scheme has outperformed the prior arts.

This paper proposes the use of genetic algorithm to select an optimal feature set for distinguishing computer graphics from digital photographic images. Our previously developed approach has derived a 234-D feature vector from each test image in HSV color space. The statistical moments of characteristic functions of the image and its wavelet subbands were selected as the distinguishing image features. Since it is possible that only certain image features contain significant information with respect to the classification, the image features with insignificant contributions to classification may be eliminated to reduce the dimensionality of the feature vectors while maximizing the classification performance. Famous for its efficiency in searching the optimal solution in a very large space, the genetic algorithm is applied to find a reduced feature set which consists of only 100-D features per image in our investigation. The experimental results have demonstrated that the 100-D reduced feature set outperforms the 234-D full feature set.

A novel reversible binary image data hiding scheme using run-length (RL) histogram modification is presented in this paper. The binary image is scanned from left to right and from top to bottom to form a sequence of alternative black RL and white RL. Combining one black RL and its immediate next white RL, we form one RL couple, thus generating a sequence of RL couples. The length of each couple is fixed during data embedding in order not to fail the reversibility. Two procedures are adopted to achieve reversibility: (1) only involve those RL couples in data embedding in which the length of couple is not shorter than threshold T1; (2) increase white RL of isolated white pixels from one to two. Another parameter T indicates where to embed data in black RL histogram. Adjusting T1 and T may result in optimum performance of pure embedding rate versus visual quality of marked image. The proposed scheme works for text, graphics, and their mixture, both halftone and nonhalftone binary images. Experimental works have shown its superior performs over the prior-arts.

JPEG image steganalysis has attracted increasing attention recently. In this paper, we present an effective Markov process (MP) based JPEG steganalysis scheme, which utilizes both the intrablock and interblock correlations among JPEG coefficients. We compute transition probability matrix for each difference JPEG 2-D array to utilize the intrablock correlation, and “averaged” transition probability matrices for those difference mode 2-D arrays to utilize the interblock correlation. All the elements of these matrices are used as features for steganalysis. Experimental works over an image database of 7,560 JPEG images have demonstrated that this new approach has greatly improved JPEG steganalysis capability and outperforms the prior arts.

In this paper, we utilize the probabilities of the first digits of quantized DCT (Discrete Cosine Transform) coefficients from individual AC (Alternate Current) modes to detect doubly compressed JPEG images. Our proposed features, named by Mode Based First Digit Features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.

Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel group's distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.

A texture image is of noisy nature in its spatial representation. As a result, the data hidden in texture images, in particular in raw texture images, are hard to detect with current steganalytic methods. We propose an effective universal steganalyzer in this paper, which combines features, i.e., statistical moments of 1-D and 2-D characteristic functions extracted from the spatial representation and the block discrete cosine transform (BDCT) representations (with a set of different block sizes) of a given test image. This novel scheme can greatly improve the capability of attacking steganographic methods applied to texture images. In addition, it is shown that this scheme can be used as an effective universal steganalyzer for both texture and non-texture images.

Computer graphics generated by advanced rendering software come to appear so photorealistic that it has become difficult for people to visually differentiate them from photographic images. Consequently, modern computer graphics may be used as a convincing form of image forgery. Therefore, identifying computer graphics has become an important issue in image forgery detection. In this paper, a novel approach to distinguishing computer graphics from photographic images is introduced. The statistical moments of characteristic function of the image and wavelet subbands are used as the distinguishing features. In addition, we investigate the influence of different image color representations on the feature effectiveness. Specifically, the efficiency of using RGB and HSV color models is investigated. The experiments have shown that the features extracted from HSV color space, which decouples brightness from chromatic components, have demonstrated better performance than that from RGB color model.

This paper presents a new steganalysis scheme to attack JPEG steganography. The 360 dimensional feature vectors sensitive to data embedding process are derived from multidirectional Markov models in the JPEG coefficients domain. The class-wise non-principal components analysis (CNPCA) is proposed to classify steganograpghy in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking modern JPEG steganographic schemes-F5, Outguess, MB1 and MB2.

In the implementation of a few JPEG steganographic schemes such as OutGuess and F5, an additional JPEG compression may take place before data embedding. The effect of this recompression on the performances of steganalyzers is experimentally studied and reported in this paper. Through a group of carefully designed experimental works, we show that the training and testing procedures adopted in classification are of great importance. An improper training and testing procedure may lead to poor steganalysis performance even for a powerful steganalyzer or an accurate performance comparison. Some other informative observations are presented in the paper as well.

Owing to the popular usage of JPEG images, the steganographic tools for JPEG images emerge increasingly nowadays, among which OutGuess, F5, and the model based steganography are the most advanced. Advancing the previous work, we present a new universal steganalysis method based on statistical moments derived from both image 2-D array and JPEG 2-D array in this paper. In addition to the first order histogram, the second order histogram is considered. Consequently, the moments of 2-D characteristic functions are also used for steganalysis. Extensive experimental works have shown that the proposed method outperforms in general the prior-arts of steganalysis methods in attacking the three aforesaid steganographic schemes.

In this paper, we present a steganalytic method to attack JPEG2000 lazy-mode steganography proposed by Su et al. The key element of the method is the Hilbert-Huang transform based analysis of the code-block noise variance sequences of stego images and non-stego noisy images. The Hilbert transform based characteristic vectors are constructed via empirical mode decomposition of the sequences and the support vector machine classifier is used in classification. Experimental results have demonstrated effectiveness of the proposed steganalytic method. According to our best knowledge, this method is the first successful attack of JPEG2000 lazy-mode steganography. And furthermore, the proposed method takes first step towards the application of Hilbert-Huang transform in steganalysis and proves its great advantage.

A steganalysis system based on 2-D Markov chain of thresholded prediction-error image is proposed in this paper. Image pixels are predicted with their neighboring pixels, and the prediction-error image is generated by subtracting the prediction value from the pixel value and then thresholded with a predefined threshold. The empirical transition matrixes of Markov chain along the horizontal, vertical and diagonal directions serve as features for steganalysis. Support vector machines (SVM) are utilized as classifier. The effectiveness of the proposed system has been demonstrated by extensive experimental investigation. The detection rate for Cox et al.'s non-blind spread spectrum (SS) data hiding method, Piva et al.'s blind SS method, and a generic QIM method (as embedding data rate being 0.1 bpp (bits per pixel)) are all above 90% over an image database consisting of approximately 4000 images. For generic LSB method (with various embedding data rates), our steganalysis system achieves a detection rate above 85% as the embedding data rate is 0.1 bpp and above

This paper presents a novel steganalysis scheme to effectively attack the JPEG steganographic schemes. The proposed method exploits the correlations between block-DCT coefficients in both intra-block and inter-block sense. We use Markov empirical transition matrices to capture these dependencies. The experimental results demonstrate that the proposed scheme is superior to the existing steganalyzers in attacking OutGuess, F5, and MB1

A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

In this paper, a new semi-fragile lossless digital watermarking scheme based on integer wavelet transform is presented. The wavelet family applied is the 5/3 filter bank which serves as the default transformation in the JPEG2000 standard for image lossless compression. As a result, the proposed scheme can be integrated into the JPEG2000 standard smoothly. Different from the only existing semi-fragile lossless watermarking scheme which uses modulo-256 addition, this method takes special measures to prevent overflow/underflow and hence does not suffer from annoying salt-and-pepper noise. The original cover image can be losslessly recovered if the stego-image has not been altered. Furthermore, the hidden data can be retrieved even after incidental alterations including image compression have been applied to the stego-image

In this correspondence, we propose a constant quality rate allocation algorithm for fine granularity scalability (FGS) coded videos. The rate allocation problem is formulated as a constrained minimization of quality fluctuation. The minimization is solved using a composite rate distortion (R-D) analysis. For a set of video frames, a composite R-D curve is first computed and then used for computing the optimal rate allocation. This algorithm is efficient because it is neither iterative nor recursive. After the composite R-D curve is computed, it can be used for optimal rate allocation of any rate budget. Moreover, the composite R-D curve can be updated efficiently over sliding windows. Experimental results have shown both the effectiveness and the efficiency of the proposed algorithm.

In this paper, a general blind image steganalysis system is proposed, in which the statistical moments of characteristic functions of the prediction-error image, the test image, and their wavelet subbands are selected as features. Artificial neural network is utilized as the classifier. The performance of the proposed steganalysis system is significantly superior to the prior arts.

This paper presents a new lossless data hiding method for digital images using integer wavelet transform and threshold embedding technique. Data are embedded into the least significant bit-plane (LSB) of high frequency CDF (2, 2) integer wavelet coefficients whose magnitudes are smaller than a certain predefined threshold. Histogram modification is applied as a preprocessing to prevent overflow/underflow. Experimental results show that this scheme outperforms the prior arts in terms of a larger payload (at the same PSNR) or a higher PSNR (at the same payload)

This paper proposed an image Steganalysis scheme based on statistical moments of histogram of multi-level wavelet subbands in frequency domain. Our theoretical analysis has pointed out that the statistical moments in frequency domain of histogram is more sensitive to data embedding than the statistical moments of histogram in spatial domain. We test the performance of our proposed scheme over non-blind spread spectrum (SS) data hiding method, blind SS method, block based SS method, LSB method and QIM data hiding methods. Besides, steganographic tools such as Outguess, JSteg and F5 are tested. The experimental results have showed that the proposed method outperforms the prior arts by Farid and Harmsen

This paper proposes an identity verification system using data hiding and fingerprint recognition. At user's home, the client's account information is encrypted and embedded into the fingerprint image via data hiding method secretly. Then the fingerprint image with embedded data is transferred to the bank over Internet. At bank side, the client's account information is extracted. It is used to retrieve the client's registered fingerprint from central database, which is then matched with extracted fingerprint via fingerprint recognition method to verify user's identity. This system is more reliable and secure than transferring password alone. The data are embedded with quantization watermark in the JPEG 2000 coding pipeline. Compare to our previous proposed system, the interaction time can be reduced because less data will be transmitted. When the fingerprint image is compressed to 1/4~1/20 of its original size, the embedded watermark can still be recovered. This system has been used in a bank pension distribution system. It can also be used in other E-business applications

This paper presents a novel feature selection scheme based on the upper bound of Bayes error under normal distribution for the multi-class dimension reduction problem. The upper bound of Bayes error in the multi-class problem is represented by the sum of the upper bound of Bayes error of every two-class pair. In order to obtain an accurate solution of the feature selection transform matrix in term of the minimum upper bound of Bayes error, a recursive algorithm based on gradient method is developed. The principal component analysis (PCA) is used as a pre-processing to reduce the intractably heavy computation burden of the recursive algorithm. The superior experimental results on the handwritten digit recognition with the MNIST database demonstrate the effectiveness of our proposed method

The security is an important issue in watermarking. It has not, however, received enough attention yet. In this paper, we propose a secure fragile watermarking algorithm based on parameterized integer wavelet transform, and the rational range of the parameter is derived theoretically. Without the parameter of the wavelet base used for watermarking, it is hard for attacker to recover or attack the hidden watermark. Multiresolution tamper detection is developed for the accurate detection. Both security and lower computational complexity of the generated fragile watermark are achieved.

Almost all the existing popular watermarking schemes model the watermarking channel noises as additive noise with zero-mean. However, our experiments show that this is not reasonable in the case of channel noise introduced by image filtering. This paper presents a new quantization-based watermarking scheme with enhanced robustness via adaptive receiving and turbo coding in addition to other measures. The present algorithm can successfully resist almost all the StirMark testing functions including both common signal processing and geometric distortions in StirMark 4.0 except for random distortion.

Recently, among various data hiding techniques, a new subset, lossless data hiding, has drawn tremendous interest. Most existing lossless data hiding algorithms are, however, fragile in the sense that they can be defeated when compression or other small alteration is applied to the marked image. The method of C. De Vleeschouwer et al. (see IEEE Trans. Multimedia, vol.5, p.97-105, 2003) is the only existing semi-fragile lossless data hiding technique (also referred to as robust lossless data hiding), which is robust against high quality JPEG compression. We first point out that this technique has a fatal problem: salt-and-pepper noise caused by using modulo 256 addition. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. This technique has been successfully applied to many commonly used images (including medical images, more than 1000 images in the CorelDRAW database, and JPEG2000 test images), thus demonstrating its generality. The experimental results show that the visual quality, payload and robustness are acceptable. In addition to medical and law enforcement fields, it has been applied to authenticate losslessly compressed JPEG2000 images.

This work proposes a unified authentication framework for JPEG2000 images, which consists of fragile, lossy and lossless authentication for different applications. The authentication strength can be specified using only one parameter called lowest authentication bit-rate (LABR), bringing much convenience to users. The lossy and lossless authentication could survive various incidental distortions while being able to allocate malicious attacks. In addition, with lossless authentication, the original image can be recovered after verification if no incidental distortion is introduced.

This paper presents a reversible data hiding method based on wavelet spread spectrum and histogram modification. Using the spread spectrum scheme, we embed data in the coefficients of integer wavelet transform in high frequency subbands. The pseudo bits are also embedded so that the decoder does not need to know which coefficients have been selected for data embedding, thus enhancing data hiding efficiency. Histogram modification is used to prevent the underflow and overflow. Experimental results on some frequently used images show that our method has achieved superior performance in terms of high data embedding capacity and high visual quality of marked images, compared with the existing reversible data hiding schemes.

In this paper, a new semi-fragile lossless digital watermarking scheme based on integer wavelet transform (IWT) is presented. Data are embedded into some IWT coefficients. The wavelet family applied is the 5/3 filter bank which serves as the default transformation in the JPEG2000 standard for image lossless compression. As a result, the proposed scheme can be integrated into the JPEG2000 standard smoothly. Different from the only existing semi-fragile lossless watermarking scheme, instead of using modulo 256 addition, this method takes special measures that prevents overflow/underflow (one of critical issue for lossless watermarking) and hence does not suffer from annoying salt-and-pepper noise. The exact cover media can be losslessly recovered if the stegoimage has not been altered. Furthermore, the hidden data can be extracted with no error even after incidental alterations, including compression, have been applied to the stegoimage (thus named after "semi-fragile").

In this letter, we present a novel objective distortion measure for binary document images. This measure is based on the reciprocal of distance that is straightforward to calculate. Our results show that the proposed distortion measure matches well to subjective evaluation by human visual perception.

In this paper, we propose a constant quality rate allocation algorithm for MPEG-4 FGS (fine granularity scalability) coded video sequences. The rate allocation problem is formulated as a constrained minimization of quality fluctuation. The minimization is solved using a novel composite rate distortion analysis. For a set of video frames, a composite rate distortion curve is first computed and then used for computing the optimal rate allocation. The proposed algorithm is very efficient because it is neither iterative nor recursive. In addition, after the composite rate distortion curve is computed, it can be used to calculate optimal rate allocation for any rate budget. Therefore, it is suitable for FGS coded bitstreams, which need to be transmitted and decoded many times at many different rates. Moreover, the composite rate distortion curve can be updated efficiently over sliding windows. This further reduces the computational complexity. Experiments using both synthetic and real FGS coded videos have shown the effectiveness and the efficiency of the proposed algorithm.

In this paper, we present a novel content-based image authentication framework which embeds the authentication information into the host image using a lossless data hiding approach. In this framework the features of a target image are first extracted and signed using the digital signature algorithm (DSA). The authentication information is generated from the signature and the features are then inserted into the target image using a lossless data hiding algorithm. In this way, the unperturbed version of the original image can be obtained after the embedded data are extracted. An important advantage of our approach is that it can tolerate JPEG compression to a certain extent while rejecting common tampering to the image. The experimental results show that our framework works well with JPEG quality factors greater than or equal to 80 which are acceptable for most authentication applications.

Robustness is a crucially important issue in watermarking. Robustness against geometric distortion and JPEG compression at the same time with blind extraction remains especially challenging. A blind discrete wavelet transform-discrete Fourier transform (DWT-DFT) composite image watermarking algorithm that is robust against both affine transformation and JPEG compression is proposed. The algorithm improves robustness by using a new embedding strategy, watermark structure, 2D interleaving, and synchronization technique. A spread-spectrum-based informative watermark with a training sequence is embedded in the coefficients of the LL subband in the DWT domain while a template is embedded in the middle frequency components in the DFT domain. In watermark extraction, we first detect the template in a possibly corrupted watermarked image to obtain the parameters of an affine transform and convert the image back to its original shape. Then, we perform translation registration using the training sequence embedded in the DWT domain, and, finally, extract the informative watermark. Experimental work demonstrates that the proposed algorithm generates a more robust watermark than other reported watermarking algorithms. Specifically it is robust simultaneously against almost all affine transform related testing functions in StirMark 3.1 and JPEG compression with quality factor as low as 10. While the approach is presented for gray-level images, it can also be applied to color images and video sequences.

This paper proposes an optimal rate-allocation scheme for fine-granular scalability (FGS) coded bitstreams that can achieve constant quality reconstruction of frames under a dynamic rate budget constraint. In doing so, we also aim to minimize the overall distortion at the same time. To achieve this, we propose a novel rate-distortion (R-D) labeling scheme to characterize the R-D relationship of the source coding process. Specifically, sets of R-D points are extracted during the encoding process and linear interpolation is used to estimate the actual R-D curve of the enhancement-layer signal. The extracted R-D information is then used by an enhancement-layer transcoder to determine the bits that should be allocated per frame. A sliding-window-based rate-allocation method is proposed to realize constant quality among frames. This scheme is first considered for a single FGS-coded source, then extended to operate on multiple sources. With the proposed scheme, the rate allocation can be performed in a single pass; hence, the complexity is quite low. Experimental results confirm the effectiveness of the proposed scheme under static and dynamic bandwidth conditions.

One of challenges encountered in information bit hiding is the reliability of information bit detection. This paper addresses the issue and presents an algorithm in the discrete cosing transform (DCT) domain with a communication theory approach. It embeds information bits (first) in the DC and (then in the) low-frequency AC coefficients. To extract the hidden information bits from a possibly corrupted marked image with a low error probability, we model information hiding as a digital communication problem and apply Bose-Chaudhuri-Hocquenghen channel coding with soft-decision decoding based on matched filtering. The robustness of the hidden bits has been tested with StirMark. The experimental results demonstrate that the embedded information bits are perceptually transparent and can successfully resist common signal processing procedures, jitter attack, aspect ratio variation, scaling change, small angle rotation, small amount cropping, and JPEG compression with quality factor as low as 10. Compared with some information hiding algorithms reported in the literature, it appears that the hidden information bits with the proposed approach are relatively more robust. While the approach is presented for gray level images, it can also be applied to color images and video sequences.

Testing and characterization of analog circuits is a very important task in the VLSI manufacturing process. However, no efficient methodology exists on how to effectively model and characterize the various faults, and even how to detect their existence. Neural networks have been successfully applied to various pattern recognition problems. In this paper, the amplitude and temporal characteristics of the good circuit response are used to train a neural network, so that it is able to distinguish between different faulty circuit responses. A Time-Delay Neural Network (TDNN) is proposed as a possible vehicle for performing the test and diagnosis.

This paper traces the development/evolution of three of our previously proposed MPEG video traffic models, that can capture the statistical properties of MPEG video data. The basic ideas behind these models are to decompose an MPEG compressed video sequence into several parts according to motion/scene complexity or data structure. Each part is described with a self-similar process. These different self-similar processes are then combined to form the respective models. In addition, the Beta distribution is used to characterize the marginal cumulative distribution (CDF) of the self-similar processes. Comparison among the three models shows that the latest model (called the simple models) is the most practical one in terms of accuracy and complexity. Simulations based on a real MPEG compressed movie sequence of Star Wars have demonstrated that the simple model can capture the ACF and the marginal CDF very closely.

Both watermark structure and embedding strategy affect robustness of image watermarks. Where should watermarks be embedded in the discrete cosine transform (DCT) domain in order for the invisible image watermarks to be robust? Though many papers in the literature agree that watermarks should be embedded in perceptually significant components, dc components are explicitly excluded from watermark embedding. In this letter, a new embedding strategy for watermarking is proposed based on a quantitative analysis on the magnitudes of DCT components of host images. We argue that more robustness can be achieved if watermarks are embedded in dc components since dc components have much larger perceptual capacity than any ac components. Based on this idea, an adaptive watermarking algorithm is presented. We incorporate the feature of texture masking and luminance masking of the human visual system into watermarking. Experimental results demonstrate that the invisible watermarks embedded with the proposed watermark algorithm are very robust.

It is estimated that video traffic will increasingly occupy a major portion of future network bandwidth, and thus traffic modeling plays an important role for network design and management. In this paper, we propose Markov modulated self-similar processes to model MPEG video sequences that can capture the LRD (long range dependency) characteristics of video ACF (auto-correlation function). The basic idea behind this modeling is to decompose an MPEG compressed video sequence into three parts according to different motion/change complexity. Each part can individually be described by a self-similar process. In addition, beta distribution is used to characterize the marginal cumulative distribution (CDF) of the video traffic. To model the whole data set, a Markov chain is used as a dominating process to govern the transitions among these three self-similar processes. Initial simulations on a real MPEG compressed movie sequence of Star Wars have demonstrated that our new model can capture the LRD of ACF and the marginal CDF very well. Video traffic synthesis using our model is presented. Further research in this direction is discussed.

Arithmetic coding with embedded channel coding (ACECC) is an arithmetic coding approach in which the code points selected over the [0-1) interval are kept within a certain minimum Hamming distance of each other. The decoding process utilizes this distance, the knowledge of the source statistics, and the self-synchronization property of arithmetic coding, to overcome channel errors. This paper explains how maximum a posteriori probability symbol decoding (MAPSD) is implemented. We show how this joint source/channel coding approach can avoid the catastrophic effect of unrecoverable errors in both random- and burst-error cases. This coding method suits band-limited channels with moderate to high error rates.

A watermark signal, which is a unique sequence of random variables, by itself does not give a decisive indication of ownership. This work addresses the need to include meaningful information such that a string of English characters, numbers, and punctuation is embedded within the watermark signal, while the signal remains a sequence of random variables. We attempt to provide answers for (1) the maximum number of symbols the watermark signal can carry, and (2) the most practical and reliable way to implement this. We approached this problem as power constrained multiple signaling over an AWGN channel.

In this correspondence, we present a new algorithm to determine optical flow that utilizes a correlation-feedback technique. Several experiments are presented to demonstrate that our method performs generally better than some standard correlation and gradient-based methods in terms of accuracy.

We propose an efficient compression algorithm for very low bit-rate video applications. The algorithm is based on (1) optical-flow motion estimation to achieve more accurate motion prediction fields; (2) DCT-coding of the motion vectors from the optical-flow estimation to further reduce the motion overheads; and (3) a region adaptive threshold technique to match optical flow motion prediction and minimize the residual errors. Unlike the classic block-matching based discrete cosine transformation (DCT) video coding schemes in MPEG 1/2 and H.261/3, the proposed algorithm uses optical flow for motion compensation and the DCT is applied to the optical flow field instead of predictive errors. Thresholding techniques are used to treat different regions to complement the optical flow technique and to efficiently code residual data. While maintaining comparable peak signal to noise ratio (PSNR) and computational complexity with that of ITU-T H.263/TMN5, the reconstructed video frames of the proposed coder are free of annoying blocking artifacts, and hence visually much more pleasant.

In this paper, we present a new algorithm of video coding for very low bit-rate applications. The algorithm is based on: (1) dense motion field, which can achieve better motion compensation than sparse (say, block-wise) motion field; (2) DCT applied to the dense motion field to drastically save overhead information; (3) region-based segmentation with morphological techniques which can segment video frames into different regions according to their content significance; (4) discrete wavelet transform (DWT) applied to residual data with adaptive bit allocation. Consequently, this algorithm avoids annoying block artifacts, thus making reconstructed video frames much more visually pleasant, while maintains similar bit-rate to H.263.

In this paper, we present a thresholding multiresolution block matching algorithm. Preventing blocks that satisfy a predefined accuracy criterion from further processing saves computation. In three experiments which have quite different motion complexities, the proposed algorithm outperforms the fastest existing multiresolution block matching algorithm. Specifically, it reduces the processing time ranging from 14% to 20%, while maintaining almost the same quality of the reconstructed image.

In this paper, a Kalman filter-based algorithm for 3-D motion estimation from a stereo image sequence using the unified temporal-spatial optical flow field (UOFF) has been proposed. The modeling problem is discussed first. More consideration has been given to determining the covariance matrices for system noise and sensor noise than the previous works using Kalman filtering in image sequence processing. The newly visible image areas, i.e., the disocclusion issue, which have not been considered in the most of previous works, are handled in our algorithm by using a threshold method. Two experiments are presented to demonstrate the effectiveness of our algorithm.>

The optical flow techniques have been developed for more than one decade. Once the optical flow field is computed accurately, this measurement of image velocity can be used widely in many tasks in computer vision area. Current computer vision techniques require that the relative errors in the optical flow be less than 10%. However, to reduce error in determination of the optical flow is still a difficult problem. In this paper, firstly, errors occurring in the correlation-based approaches to optical flow computation are analyzed. Through understanding how the errors arise, we developed a new approach to computation of optical flow named the correlation-feedback approach. It is based on the idea of feedback and the correlation-based approach. In this approach, a virtual continuous image is obtained by a bilinear interpolation applied to a digital image. The idea of feedback is used so that errors in determining optical flow are reduced considerably in the iterative procedure. It is proved that the algorithm is convergent generally. Several experiments working on real image sequences in the laboratory demonstrate that our correlation-feedback algorithm performs better than the gradient-based and correlation-based algorithms in terms of accuracy.>

In the above-mentioned work by S. Basu (see ibid., vol.78, p.614-626, 1990), one of the main focal points is the robustness of positive property for a k-variate interval rational function having complex coefficients (hereafter the property is referred to as the PC property). It is concluded that the PC property of the specific 16(2/sup k/)/sup 2/ extreme members of the set can imply the PC property of the set. In this comment, it is proven that the PC property of a k-variate complex interval rational function can be assured by the PC property of its certain 16(2/sup k/) extreme members, which are a proper subset of those 16(2/sup k/)/sup 2/ extreme members defined in the above-mentioned work.>

The authors consider the factorization of the 1-D and 2-D spectral density functions (z-transforms of real-valued autocorrelation sequences), S(z) and S(z/sub 1/, z/sub 2/) in the forms Sz=F(z)F(z/sup -1/) and S(z/sub 1/, z/sub 2/)=F(z/sub 1/, z/sub 2/) F(z/sub 1//sup -1/, z/sub 2//sup -1/), respectively where the coefficient of polynomials F(z) and F(z/sub 1/, z/sub 2/) are constrained to be nonnegative. The problem is solved only in special cases. In the general situation, the scopes for adapting and generalizing the iterative techniques available for the classical 1-D spectral factorization problem to tackle the nonnegativity constrained spectral factorization problem under study have been analyzed.>

This paper considers the generalization of 1-D iterative methods to the 2-D case in order that 2-D spectral factorization may be iteratively implemented. The 2-D Bauer generalization has been proposed by other authors. The authors of this paper have obtained meaningful generalizations of Wilson's and Arp's algorithms. Comparisons of the proposed generalizations bring out the advantages of the 2-D Wilson scheme in terms of guaranteed stability of the spectral factor coupled with accuracy and speed of convergence.