Image-in-image hiding, which embeds a full-size secret image into a cover image with minimal perceptual distortion and accurate recovery, has attracted increasing attention because of its wide applications in copyright protection, covert communication, and digital forensics. However, when facing full-size secret images, the secret data often exceed the capacity limitation of individual cover images, resulting in existing solutions struggling to achieve an effective balance among robustness, high load capacity, and resistance to steganalysis and detection capabilities, which is particularly prominent when encountering social noise interference in real-world scenarios. To address these challenges, we propose MambaRIS, a robust and efficient image steganography framework that combines the dual-tree complex wavelet transform (DTCWT) with a state space model. The DTCWT module enables directionally selective decomposition of the input, enriching frequency-domain representations and providing more resilient embedding regions for robust cross-frequency hiding and recovery. Furthermore, we introduce a Mamba-based autoencoder architecture equipped with a novel spatial channel Mamba block (SCMB), which integrates spatial and channel attention mechanisms with linear-time global dependency modeling, significantly improving embedding adaptability under complex noise and distortion conditions. Extensive experiments demonstrate the superiority of the proposed scheme in terms of visual quality, robustness, and resistance to steganalysis. In JPEG compression with a quality factor of Q = 80, our method achieves an average improvement in hiding and recovery accuracy (measured by PSNR) of 0.87 dB compared with state-of-the-art architectures, while reducing the number of parameters by 33%.

As societal focus on image authenticity grows, image manipulation localization has become a crucial and challenging task in computer vision. Current methods relying on dual-stream encoders to extract features from both RGB and noise images often suffer from feature misalignment and information loss during fusion. Moreover, many localization methods use loss functions to identify manipulated areas, but balancing weights between manipulated regions and edges remains challenging. To address these challenges, we propose a novel method that integrates features in dual-stream networks with adaptive selective state spaces. By treating the two output features from the dual-stream encoder as system inputs, we construct a feature space that optimizes the system’s state space. Introducing temporal dynamics enriches the feature representation and enhances learning capabilities, significantly improving the accuracy and reliability of image manipulation localization. Additionally, we propose an edge residual review module that refines the boundaries of manipulated regions from the preliminary output, subsequently enhancing the input features for improved re-localization accuracy. Extensive experiments demonstrate that our approach yields competitive results on diverse large-scale image datasets, outperforming most state-of-the-art methods in both precision and robustness.

False data injection attacks (FDIAs) refer to attackers exploiting vulnerabilities in the detection of bad data in smart grid energy management systems to maliciously manipulate state estimation results in the cyber-physical system, resulting in unstable operation of the power system. Existing deep learning-based detection schemes often fail to capture the spatial topology features and long-term dependencies in power grid data well. Meanwhile, the complexity of deep detection models makes them a "black box", reducing the credibility of detection results. To address the aforementioned challenges, this paper presents a dependency-aware deep interpretable FDIA detection model. The proposed model firstly introduces Graph Sample and Aggregate (GraphSAGE) network to extract spatial topological features, which are used to represent the deep spatial topological dependencies of adjacent data nodes. Subsequently, we build a Bidirectional Long Short-Term Memory (BiLSTM) network with a Squeeze-and-Excitation (SE) attention module, which can efficiently aggregate attack characteristics and long-term dependency information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the SHapley Additive exPlanations (SHAP) method is used to demonstrate the interpretability of the model in the spatial-temporal dimensions and then provide the basis for high-precision detection results. A series of extensive experiments are carried out over the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model presents a superior overall performance comparing with several state-of-the-art FDIA detection models, and provides reasonable interpretability from the spatial-temporal dimensions.

Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.

Although screen-camera resilient watermarking addresses issues such as privacy leakage and copyright infringement in digital images to some extent during screen-camera communication. However, in screen-camera scenarios, uncontrolled shooting environments, various display devices, and different lens types introduce more complex noise into the watermarked images. Because some noise generated during the screen-camera process cannot be quantitatively analyzed, the integrity of the embedded watermark is compromised, making copyright verification and information acquisition still difficult. To solve this problem, we establish a large-scale screen-camera image dataset (SCISet) and propose a noise simulation network (NoS-Net). Specifically, we obtain 36,000 screen-camera images under various shooting environments with multiple types of screens and cameras. Then, we use SCISet to train the proposed NoS-Net based on the U-Net architecture, which can learn multi-level and complementary feature information of screen-camera images, enhancing its ability to simulate complex noise. Experimental results show that integrating the proposed NoS-Net into mainstream screen-camera resilient watermarking methods significantly improves their ability to resist screen-camera noise attacks. Furthermore, the diversity of SCISet plays an important role in advancing robust watermarking research.

Multi-image steganography refers to the technique of embedding multiple secret images into a single cover image while ensuring that the secret images remain imperceptible and can be perfectly recovered by the recipient. Traditional single-image-based steganography often leads to noticeable contour shadows or color distortions in the cover image, making the hidden image more detectable. In contrast, cascaded invertible neural network-based steganography introduces a large number of parameters, complicating the network structure and resulting in a time-consuming learning and training process. To address the above problems, this paper proposes a novel flow-based, end-to-end multi-image invertible steganography framework (StegFlow), which effectively integrates forward and backward data flows for image hiding and recovery. The framework employs cascading operations to enable deep hiding of multiple secret images. To enhance the coupling capabilities, we introduce an invertible permutation layer that disrupts the channel arrangement order, allowing the coupling layer to more accurately guide the embedding of secret information into regions of the image that are easy to hide and recover. In addition, a high-frequency distribution mapping (HFDM) is designed to model the lost high-frequency information during image hiding process, significantly improving the recovery performance of the secret images. Extensive experiments are performed over multiple classical datasets, and the results demonstrate that compared to state-of-the-art (SOTA) models, the proposed framework can achieve a superior overall performance in terms of visual quality and anti-steganalysis capability. Specifically, our scheme can improve the hiding accuracy (measured by PSNR) by over 3 dB and the recovery accuracy by over 1 dB when hiding two secret images.

JPEG reversible data hiding (RDH) refers to covert communication technology to accurately extract secret data while also perfectly recovering the original JPEG image. With the development of cloud services, a large number of private JPEG images can be efficiently managed in cloud platforms by embedding user ID or authentication labels. Nevertheless, data embedding operations may inadvertently disrupt the encoding sequence of the original JPEG image, resulting in severe distortion of the host image when it is re-compressed to JPEG format. To address this problem, this paper proposes a new JPEG RDH scheme based on block sorting optimization and dynamic iterative histogram modification. We firstly design a block ordering optimization strategy by combining the number of zero coefficients and the quantization table values of non-zero coefficients in a DCT block. Subsequently, a dynamic iterative histogram modification scheme is proposed by considering the local features and embedding capability of histograms generated from different texture images. According to the given payloads, we introduce different parameters to control the iterations of two-dimensional histogram and then adaptively generate the optimal histogram modification mapping, which can realize low JPEG file size increments by guaranteeing most of the AC coefficients unchanged as much as possible. Numerous experiments have shown that our scheme can achieve an effective balance among embedding capacity, visual quality, file size increment, computational complexity, and outperforms the state-of-the-arts in terms of the above metrics.

Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.

With the development of social network, a large number of private JPEG images are stored in social cloud platform. Correspondingly, the platform embeds user ID or authentication labels to manage these privacy images, preventing them from being arbitrarily accessed or tampered by illegal persons. However, data embedding in JPEG domain inevitably produces irreversible modifications to DCT coefficients, thus resulting in obvious or even serious distortion in the host JPEG images. To address this problem, this paper proposes an efficient JPEG reversible data hiding (RDH) method by constructing progressive two-dimensional histogram mappings. We firstly design distortion function to calculate the cost of each DCT frequency band, and then sort them to build histogram mapping containing a series of coefficient pairs. Subsequently, a progressive mapping mechanism is introduced to maintain most of AC coefficients unchanged. According to the given capacity, this mechanism can adaptively generate an optimum two-dimensional histogram mapping to embed secret messages. Our scheme can achieve an effective balance among embedding capacity, visual quality of the marked image and file size expansion, while keeping high cost-performance complexity. Extensive experiments demonstrate that our method outperforms existing JPEG RDH schemes in terms of visual quality and file size increment of the marked image, and provides an efficient solution for the confidentiality and security access problem of sensitive private image in cloud environment.

Batch steganography regarding to image-selection and payload-allocation has gained increasing attention due to the secure demanding of data hiding of real scenario. However, due to the predefined selection mechanism, the chosen images are always complex which means that the diversity of the selected cover set is finite. In this paper, we develop a diverse and secure batch steganography scheme including the model-based generation and double-layered payload assignment. To construct the diverse image set, we use the Kullback-Leibler (KL) divergence to quantify the diversity increment and, relying on steganographic distortion, we select multiple image subsets (class) to create the diverse cover set in which each subset is modelled as the normal distribution with proper model parameters. Depending on the distortion of image subset, we assign the payload into all subsets with between-class allocation. Moreover, for the assigned payload of each subset, we introduce the linear model to achieve the within-class allocation. Finally, we obtain a diverse cover set along with suitable payload. Extensive experiments demonstrate the practicality of the proposed method in diversity and, compared with other selection methods, exhibit higher security on multiple steganalytic tools.

Existing effective cover selection methods aim to select the complex images as covers to achieve the highly security with the aid of the embedding distortion computed from a natural image. However, the calculation of the embedding distortion divulges the image content to a steganographer. To overcome this issue, this work proposes a novel cover selection scheme in encrypted images to achieve the image content-protection and cover-selection simultaneously. In the first phase, the content owner encrypts several most significant bits (MSBs) of each image using an encryption key and the encrypted image is shuffled by block. Meanwhile, with a sampling key, the content owner selects some encrypted blocks and outputs them to the steganographer. In the second phase, the steganographer calculates first-order noise residuals of adjacent pixels of the acquired blocks along different directions. Importantly, we design a texture descriptor named as structured Local binary pattern (SLBP) to encode all the residuals by which the images owing the maximal SLBP values are chosen as the optimal covers. We demonstrate the security of our proposed scheme on multiple steganographic and steganalytic methods and the extensive results show that our scheme exhibits excellent performance without knowing of the original image content. Moreover, the results testify that the designed SLBP achieves the perfect evaluation of image complexity.

This paper tackles a recent challenge in designing an efficient steganographic distortion model, whose goal is to accurately measure the modification cost of a pixel and help design steganographic schemes with high undetectability. Existing distortion models mostly assume that different modification directions of a pixel have an identical cost value and that pixel modifications are independent. These assumptions, however, may not lead to good steganography design because the modification direction of neighbouring pixels may affect the cost measurement of the current pixel. To address this problem, we propose a new distortion calculation method using dynamic cost correction and multi-modality distortion ensemble. The proposed scheme first employs a given distortion model to generate the original cost map. The cost of each pixel is then dynamically adjusted with majority voting according to the modification directions of its neighbouring pixels. Furthermore, different distortion calculation models are integrated to make the final decision on the distortion of each pixel. Experimental results show that compared to existing additive distortion-based steganographic schemes and deep learning-based steganographic schemes, steganography using our proposed distortion model performs better when tested against state-of-the-art steganalysis methods.

This paper introduces a novel, lightweight deep invertible steganography network (LiDiNet) for image-in-image steganography. Traditional methods, while hiding a secret image within a cover image, often suffer from contour shadows or color distortion, making the secret image easily detectable. Additionally, the superposition of multiple invertible networks may complicate network structures and introduce excessive parameters, making the network training and learning processes difficult. LiDiNet addresses these issues by employing multiple invertible neural networks (INNs) to create a pair of coupled invertible processes for image hiding and recovery. A key innovation is the invertible convolutional layer, which streamlines the affine coupling structure in each INN for improved information fusion. In addition, a series of adaptive coordination spatial-wise attention modules are integrated to enhance the network’s effectiveness in image hiding and recovery, thereby elevating the security of the steganography. LiDiNet’s lightweight structure ensures both high-capacity steganography and robustness against steganalysis. Extensive experiments across various image datasets demonstrate LiDiNet’s superior performance, particularly in visual quality and anti-steganalysis capability, compared to existing methods.

Multi-image steganography refers to a stegano- graphic method where a user tries to hide multiple confidential images within a single cover image, and all confidential images can be correspondingly recovered perfectly by the recipient. Multi-image steganography essentially belongs to a high-capacity image steganographic scheme, but such high hiding capacity may easily cause severe contour shadows or color distortion of steganographic images, resulting in a significant reduction in anti-steganalysis capability. To address the above problem, this article designs a deep invertible neural network by introducing spatial-channel joint attention mechanism, in which the confidential image hiding and recovery can be regarded as a pair of coupled invertible processes. Specifically, a series of simple invertible networks having the same structure are firstly used to construct a cascaded deep invertible neural network framework, in which multiple confidential images can be sequentially embedded into a single cover image through a series of flexible cascaded iterative operations. Subsequently, spatial-channel joint attention module is designed to re-construct invertible network model, which can guide the embedding of secret information into more secure image regions. Accordingly, this joint attention mechanism can effectively address the problem of visual quality and security degradation of steganographic images due to high embedding capacity. Extensive experiments demonstrate that our scheme can obtain superior performance over different large-scale image sets, and outperforms state-of-the art methods with higher visual quality and stronger anti-steganalysis capability.

In this article, an effective print-camera (P-C) resistant image watermarking scheme is proposed. To achieve watermark robustness, most of existing works try to simulate P-C noise by a sophisticated math model. However, the diversity of P-C noises in the real world is ignored, and the watermarked image may not attain a good balance between high robustness and low distortion. To address the problem, we construct an efficient end-to-end network architecture for watermark embedding and extraction. To be specific, a deep noise simulation network (NSN) is designed to simulate the fusion process of real P-C noises, which can help to generate high-robust watermarked image. Also, a multitask loss function based on just-noticeable-difference (JND) is proposed to conduct constrained learning for residual image containing watermark information, thus, the distortion of generated watermarked image can be significantly reduced. Experimental results show that our scheme can achieve high robustness against P-C process while maintaining a satisfactory watermark capacity and visual quality of watermarked image.

JPEG image manipulation localization aims to accurately classify and locate tampered regions in JPEG images. Existing image manipulation localization schemes usually consider diverse data streams of spatial domain, e.g. noise inconsistency and local content inconsistency. They, however, easily ignore an objective scenario: data stream features of spatial domain are hard to directly apply to compressed image format, e.g., JPEG, because tampered JPEG images may contain severe re-compression inconsistency and re-compression artifacts, when they are re-compressed to JPEG format. As a result, the traditional localization schemes relying on general data streams of spatial domain may result in a large number of false detection of tampered region in JPEG images. To address the above problem, we a new JPEG image manipulation localization scheme, in which a wide-receptive-field attention network is designed to effectively learn JPEG compressed artifacts. We firstly introduce the wide-receptive-field attention mechanism to re-construct U-Net network, which can effectively capture contextual information of JPEG images and analyze tampering traces from different image regions. Furthermore, a flexible JPEG compressed artifact learning module is designed to capture the image noise caused by JPEG compression, in which the weights can be adjusted flexibly based on image quality, without the need for decompression operations on JPEG images. Our proposed method can significantly strength the differentiation capability of detection model for tampered and non-tampered regions. A series of experiments are performed over different image sets, and the results demonstrate that the proposed scheme can achieve an overall localization performance for multi-scale JPEG manipulation regions and outperform most of state-of-the-art schemes in terms of detection accuracy, generalization and robustness.

Federated learning is an efficient technology that implements distributed model training among multiple data sources with local data, and can realize data privacy protection and data sharing computing .However, existing federated learning models may involve a large number of external attacks that can reconstruct the original training data using the acquired model, resulting in possible global model or user privacy data attacks. To address the above problem, we propose a new decentralized multiconsensus federated learning model by combining blockchain and interplanetary file system (IPFS), named as BIFLC. To be specific, we firstly design an on-chain consensus process based on a blockchain hybrid consensus mechanism by introducing a proof- of-work (PoW) and a proof-of-stake (PoS) mechanism, which can ensure the integrity of the on-chain consensus process and provide a chained data hash index for data. Furthermore, we introduce the interplanetary file system to reduce the cost of storing data on the chain and employ its distributed content delivery mechanism to save bandwidth. Extensive experiments demonstrate that our proposed scheme has higher accuracy and lower IPFS transmission time.

Image manipulation localization is a technique that can efficiently segment the tampered regions from a suspicious image. Existing work usually trains a detection model by fusing the features from diverse data streams, e.g., noise inconsistency, recompression inconsistency, and local inconsistency. They, however, ignore a fact that not all tampered images contain these data streams. As a result, high feature redundancy may cause a large number of false detection for tampered region. To address this problem, this paper designs an end-to-end high-confidence localization network architecture. First, deep convolutional neural networks are utilized to extract multi-scale feature sets from the RGB streams. We then design a semantic refined bi-directional feature integration module to fully fuse multi-scale adjacent features and significantly enhance feature representation. Subsequently, morphological operations are introduced to extract multi-scale edge information, which can efficiently reduce feature redundancy by generating wider high-resolution edges during image reconstructing. Finally, a deep semantic residual decoder is sequentially re-constructed by spreading deep semantic information into each decoding stage. The proposed method can not only improve the manipulation localization accuracy, but also guarantee the model robustness. Extensive experiments demonstrate that our method can obtain an effective performance in locating forged regions over different large-scale image sets, and outperforms most of state-of-the-art methods with higher localization accuracy and stronger robustness.

JPEG image encryption aims at effectively converting the original JPEG image into a noise-like image that does not contain any useful information of original image. Existing schemes for JPEG image encryption, however, may not attain a good balance in terms of file size increment and encryption security. To address the problem, we design a novel JPEG image encryption scheme. Different from existing schemes, we first predict DC coefficients by an adaptive prediction method. Subsequently, the histogram of DC coefficient prediction errors is encrypted by combining the prediction errors and random integers to reduce the encoded length, which can ensure a very small increment of file size. Furthermore, we construct the RS (run/size) pairs in each DCT block and then implement the permutation for both RS pairs extracted from the upper left corner of each DCT block and all DCT blocks excluding DC coefficients, which can further distort the image contents. Extensive experiments demonstrate that, compared with existing JPEG image encryption schemes, our scheme can ensure not only the JPEG format compatibility for encrypted image, but also keep a very small file size increment and the superior security performance.

In this paper, we propose an enhancing steganographic scheme by random generation and ensemble stego selection. Different from existing steganography that only focuses on distortion function designing, our scheme considers both distortion model and optimized stego generation. In specific, for given cover, we firstly train an universal steganalyzer to calculate its gradient map, which is referenced to randomly adjust cost distribution of this cover. Multiple candidate stegos are sequentially generated by combining adjusted cost and syndrome trellis coding. Furthermore, we build an ensemble selection mechanism to effectively determine the candidate that is closest to the statistical characteristics of cover image as the final steganographic image. Comprehensive experiments demonstrate that compared to existing state-of-the-art schemes, our scheme can significantly boost the anti-steganalysis capability.

JPEG Reversible data hiding (RDH) aims at correctly extracting hidden data and perfectly recovering the original JPEG image. By combining different frequency selection and block ordering manners, appropriate AC coefficients can be determined to achieve an efficient JPEG RDH scheme using histogram shifting (HS) mechanism. Nevertheless, existing methods only rely on rough frequency band distortion model by involving all DCT blocks, ignoring the dynamic influence of block ordering on embedding block selection. As a result, JPEG image containing hidden data may have an obvious distortion and file size expansion due to an inappropriate frequency band selection. To address the aforementioned problem, we design a new JPEG RDH scheme to optimize distortion model with frequency priority reassignment. Our method firstly orders all DCT blocks by referring to zero AC coefficients of each block. Subsequently, based on the given secret data, only partial ordered blocks are selected to calculate the distortion of each DCT frequency, which can further optimize unit distortion caused by shiftable coefficients. Furthermore, we employ the adjustment parameter to adaptively reassign the priority of each frequency band based on its influence on file size expansion, which can significantly reduce file size expansion of the marked JPEG image. Extensive experiments demonstrate that our method outperforms existing JPEG RDH methods with better visual quality and smaller file size increment of the marked image.

High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.

This paper develops a retrieval scheme for encrypted JPEG images based on a Markov process. In our scheme, the stream cipher and permutation encryption are combined to encrypt discrete cosine transform (DCT) coefficients for protecting JPEG image content’s confidentiality. And thus, it is easy for the content owner to achieve the encrypted JPEG images uploaded to a database server. In the image retrieval stage, although the server does not know the plaintext content of a given encrypted query image, he can still extract image feature calculated from the transition probability matrices related to DCT coefficients, which indicate the intra-block, inter-block, and inter-component dependencies among DCT coefficients. And these three types of dependencies are modeled by the Markov process. After that, with the multi-class support vector machine (SVM), the feature of the encrypted query image can be converted into a vector with low dimensionality determined by the number of image categories. The encrypted database images are conducted similarly. After low-dimensional vector representation, the similarity between the encrypted query image and database image may be evaluated by calculating the distance of their corresponding feature vectors. At the client side, the returned encrypted images similar to the query image can be decrypted to the plaintext images with the help of the encryption key.

This work proposes a spatial steganalysis scheme based on local textural features and double dimensionality reduction. First, an image is filtered by multiple filters to obtain a number of residual images. Local textural patterns are obtained by comparing the pixel values with the neighbors' value in each residual image. By combining all local textural patterns, a high-dimensional textural feature set is formed. Then, principal component analysis is used to perform double dimensionality reduction for high-dimensional textural features. In the first dimensionality reduction stage, the correlation from the same filter is eliminated, while the correlation from different filters can be also eliminated in the second dimensionality reduction stage. Finally, a textural feature set with low dimensionality is proposed and can be effectively used in steganalysis. Experimental results show that proposed textural feature set can efficiently detect adaptive steganographic schemes in spatial domain. Copyright © 2014 John Wiley & Sons, Ltd.

This letter proposes a novel scheme for spatial steganalysis based on contrast of residuals (CoR). After selecting complex blocks from an uncompressed image by a fluctuation function, the residuals are calculated from the selected blocks and the whole image after applying diverse filters. The CoR is represented as an angle and the norm of residuals is considered as the corresponding weight of angle, which is used as the new steganalysis feature. In the proposed scheme, no quantization and truncation is required and the effective information of long-range dependencies among pixels is kept properly. Also, the dimensionality of feature is linear with the number of residuals. The accuracy of proposed scheme is evaluated on HUGO and WOW algorithms, and the experimental results show that the proposed CoR feature has superior performance at low embedding rate with lower dimensionality.

This paper tackles a recent challenge in identifying culprit actors, who try to hide confidential payload with steganography, among many innocent actors in social media networks. The problem is called steganographer detection problem and is significantly different from the traditional stego detection problem that classifies an individual object as a cover or a stego. To solve the steganographer detection problem over large-scale social media networks, this paper proposes a method that uses high-order joint features and clustering ensembles. It employs 250-D features calculated from the high-order joint matrices of Discrete Cosine Transform (DCT) coefficients of JPEG images, which indicate the dependencies of image content. Furthermore, a number of hierarchical sub-clusterings trained by the features are integrated as a clustering ensemble based on the majority voting strategy, which is used to make optimal decisions on suspicious steganographers. Experimental results show that the proposed scheme is effective and efficient in identifying potential steganographers in large-scale social media networks, and has better performance when tested against the state-of-the-art steganographic methods.

This work presents a retrieval scheme for encrypted JPEG images based on Markov process. In our scheme, the stream cipher and permutation encryption are combined to encrypt JPEG images, which are then uploaded to a database server. After that, the server without knowing the original content can extract features from the transition probability matrices of the AC coefficients of encrypted query image, in which those coefficients are modeled by Markov process. With the multi-class support vector machine (SVM), the features of encrypted query image can be converted into a vector with low dimensionality determined by the number of image categories. The encrypted database images are conducted similarly. After low-dimensional vector representation, the similarity between encrypted query image and database image may be measured by calculating the distance of their corresponding vectors. At the client side, the encrypted images returned by the server are decrypted to the plaintext images using encryption key. The proposed scheme can preserve file compliance and file size for encrypted JPEG images, while providing privacy-preserving image retrieval.

This work proposes a JPEG steganalytic scheme based on high-dimensional features and Bayesian ensemble classifier. The proposed scheme employs 15700 dimension features calculated from the co-occurrence matrices of DCT coefficients and coefficient differences, which indicate the intra-block and inter-block dependencies of image content. Furthermore, a number of sub-classifiers trained on the features are integrated as an ensemble classifier with a Bayesian mechanism, which is used to give optimal decisions for suspicious images. Experimental results show that both the high-dimensional features and the Bayesian mechanism contribute to the extended scheme, and the performance of the extended scheme is better than those of previous schemes.