Reversible data hiding (RDH) for JPEG images, particularly those focusing on DCT coefficient modification, has garnered significant attention in recent years. Existing methods primarily select coefficients valued$\pm 1$for expansion embedding to avoid significant file size increases caused by modifying zero-valued DCT coefficients. However, zero-valued coefficients, which constitute the majority of DCT coefficients, are more suitable for data embedding to reduce the shift distortion. To efficiently utilize zero-valued coefficients for high-capacity embedding while controlling the file size increment, this paper introduces a novel JPEG RDH method based on ternary matrix embedding, where ternary syndrome trellis codes (STC) is employed on selected zero-valued coefficients to minimize the expansion embedding distortion, and other non-zero-valued coefficients are shifted for reversibility. Furthermore, a novel DCT coefficients measurement strategy is proposed for coefficient selection to further reduce the shift distortion. Extensive experimental validations demonstrate the superiority of the proposed method in various evaluation criteria. Notably, the proposed method achieves more than twice the embedding capacity of some state-of-the-art methods at the same PSNR while maintaining file size increment within acceptable bounds.

Transferable adversarial examples (AEs) have attracted considerable attention due to their ability to expose vulnerabilities in black-box deep neural networks (DNNs). However, achieving superior transferability for targeted attacks remains a challenge. In this article, inspired by the observation that AEs with smaller intra-class distances and larger inter-class distances tend to exhibit higher transferability, we propose a novel targeted attack based on Feature Contrastive Optimization (FCO). This attack enhances adversarial transferability by minimizing intra-class distances and maximizing inter-class distances. Specifically, we first define positive samples (belonging to the target class) and negative samples (belonging to non-target classes) that correspond to targeted AEs. Subsequently, leveraging these defined positive and negative samples, we propose two metrics—Intra-class Compactness (IC) and Inter-class Separability (IS)—to construct a novel Feature Contrastive (FC) loss. By integrating this plug-and-play FC loss into standard adversarial objectives, the generated AEs are encouraged to better align with the target class distribution while diverging from those of non-target classes. Extensive experiments on the ImageNet-compatible dataset demonstrate that our approach consistently improves targeted transferability across a broad range of DNN architectures.

This study systematically uncovers and quantitatively evaluates the pervasive Orientalist biases in text-to-image (T2I) and text-to-video (T2V) generation models through a sociocultural lens grounded in postcolonial Orientalist theoretical frameworks. We identify systematic biases in the visual representations produced by multimodal generative models, including hyper-exoticization and temporal alienation. These biases mirror colonial-era narratives and undermine equitable sociocultural communication. Through empirical analysis of 8 mainstream T2I models and 4 T2V models, we demonstrate that culturally neutral prompts related to China consistently generate visual outputs embedded with Orientalist biases. We develop a novel visual question answering (VQA) framework as an evaluation metric, leveraging state-of-the-art vision-language model (VLM) to establish the first automated quantitative assessment methodology for such biases. A mitigation framework employing large language model (LLM) is proposed and experimentally validated. This interdisciplinary work illuminates the societal implications of multimodal generative models while advancing efforts toward fair and inclusive social computing.

The widespread emergence of Deepfake content has made it increasingly important to distinguish real and fake faces. Although many methods focus on detecting Deepfake content, only a few address the protection of real faces against forgery. Therefore, this paper proposes a proactive Deepfake detection scheme named FALU, which combines the uniqueness of facial identity features with the robustness of average hashing. The method first divides the input image into facial and non-facial regions, extracts identity-related features from the facial region, encodes them using average hashing, and embeds the result as a watermark into the non-facial region. During detection, the watermark is extracted from the non-facial region and compared with a newly generated hash code from the facial region. High correlation indicates authenticity, while low correlation suggests Deepfake forgery. To facilitate efficient and reliable watermark embedding, FALU integrates the symmetric sampling structure of U-Net with Mamba-like linear attention mechanism, proposing a lightweight encoder network. This scheme ensures the persistent presence of secret information before and after manipulation, thereby enhancing face source detection and tampering identification. Experimental results demonstrate that the proposed scheme effectively counters traditional Deepfake techniques and shows significant potential for preserving personal privacy.

Aiming to mitigate image distortion caused by steganography algorithms at high-capacity information embedding and enhance the steganalysis resistance capability of generated stego images, this paper proposes a high-capacity differential steganography algorithm for color images based on multiple adversarial networks. Instead of directly modifying the pixels of the cover image, the algorithm embeds the secret information into the differential plane generated by the two most similar channels of the cover image. Consequently, the distortion of the stego image is minimized while embedding a secret image of the same size. At the same time, the fidelity of the stego and extracted secret images is continually improved through adversarial training between the generator and discriminator in the proposed steganography network. Furthermore, multiple steganalysis networks are parallelly utilized to enhance the steganalysis resistance capability of stego images. In addition, the Lion optimizer is utilized for the first time to improve the convergence speed of the proposed steganographic network. Experimental results show that the comprehensive performance of the proposed algorithm outperforms other state-of-the-art steganography algorithms significantly.

Robust video watermarking refers to the ability to extract the originally embedded watermark information from a video even after malicious modifications and attacks. Currently, traditional watermarking methods have the drawback of lacking robustness against multiple watermark attacks simultaneously. Neural network-based approaches have not fully considered the multi-scale features of videos and tend to lose information during the fusion of scale features. Therefore, we propose a video watermarking scheme based on Channel Spatial Attention. Our model can extract feature information at different scales, allowing the watermark to adapt to features of different scales in the video. Through a series of comparative experiments, our method has shown significant improvements over traditional video watermarking methods and deep learning-based video watermarking models.

The popularity of Deepfake technology has raised the challenge of recognizing real and fake faces. While detection methods already exist, most of them are passive forensics and face challenges of generalizability and migration. Currently, some research attempts to protect the original image by priorly inserting invisible information. However, there are still shortcomings in terms of image quality and information robustness due to information embedding, i.e., watermarking. Therefore, we employ the robustness of perceptual hash coding and combine it with information hiding techniques to propose a proactive Deepfake detection solution, referred to as PWPH in this paper. Our approach is simple and efficient: first, the image containing a face is divided into two parts: FA (face area), and NFA (non-face area). A perceptual hash code is generated from the non-face area (NFA). Then, the hash codes are embedded as watermarks into the FA. At the extraction stage, we use the same method as the encoder to retrieve the embedded watermark from FA. The watermark is then compared with the hash code generated from the NFA of the detected image. The extracted watermark is sensitive to distortion and may vanish during Deepfake processing. Experimental results validate that our method, requiring just one encoder and decoder, enables active detection and source tracking. Furthermore, its efficacy in typical Deepfake scenarios such as face swapping and expression reconstruction is confirmed through comparison with prior arts.

Aiming at the problem of most Robust Reversible Data Hiding (RRDH) schemes failing to anti geometric deformation attacks, a new RRDH algorithm based on Polar Harmonic Fourier Moments (PHFMs) is presented in this paper, thereby enhancing both the robustness of the embedded data and perceptual quality of the data-embedded image. Firstly, by leveraging the anti-geometric transformation and high-fidelity features of PHFMs, the image is transformed into its frequency domain for RRDH. Then, a quantitation index modulation (QIM) algorithm is designed to embed secret data into the integer part of PHFMs coefficients. By minimizing the differences between the secret-data-embedded image and the original image, the amount of compensation data is reduced. Meanwhile, a two-dimensional RDH scheme is further adopted to embed the compensation data, thus reducing the distortion of the full data-embedded image. Finally, the robustness of the embedded data and the fidelity of the full data-embedded image are both improved. The combination of PHFMs transformation and two-dimensional RDH enables the proposed RRDH algorithm to achieve high visual quality and strong resistance capability against geometric transformation attacks. Extensive experimental results demonstrate that the proposed RRDH algorithm outperforms other state-of-the-art techniques.

The photo response non-uniformity (PRNU) is used to connect an image to its source sensor. In this paper, researchers propose a PRNU anonymity method based on image segmentation to cut the relationship between the image and its source camera. According to the distribution rule of PRNU in the high and low frequency band of the image, the high and low frequency information of the part is also processed differently, which ensures the quality of the output image to a large extent. Experiments on the datasets show that the proposed method can preserve the biometric characteristics of the device while maintaining the anonymity of the device. Comparing with prior art, peak signal to noise ratio (PSNR) and cosine similarity are improved by 1.9 dB and 0.02 points, respectively.

Abstract This paper proposes a Gaussian Distance Intersection over Union (GDIoU) loss function‐based YOLOv4 deep learning network to solve the problem of slow speed and low accuracy insulator location in power facilities health inspection. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network; at the same time, the GDIoU loss is added as one part of the network propagation loss, and the insulator's location accuracy is accordingly improved. Moreover, a re‐location scheme for tilt insulators correction is proposed to enhance the location accuracy of the insulators in different spatial angle states. Large amounts of field insulator images were gathered as training and testing samples to evaluate the performance of the proposed scheme. The experimental results have demonstrated that the GDIoU‐based YOLOv4 deep learning network combined with the tilt correction scheme can improve the insulator location speed by three times compared with the peer schemes, and the average precision is increased by 7.37% compared with the naive YOLOv4 network. The performance of the proposed scheme meets the requirement of online insulator location adequately.

Accurate registration of multispectral satellite images is a challenging task due to the significant and nonlinear radiometric differences between these data. To address this problem, this letter explores the strategy of geometrical similarity between triplets of feature points, and it is combined with the structural similarity between images in a feature-based image registration framework. The underlying principle is that the structural and geometrical similarities generally preserve across the images being registered. In this feature-based image registration framework, a set of control points (CPs) are first detected. Then, the geometric similarity between triplets of CPs is defined, followed by a ranking operation of these triplets of CPs. The highly ranked triplets are used to estimate a spatial transformation between images. Finally, initial matches obtained by a benchmark registration technique are refined by the estimated transformation. The experimental results demonstrate the great effectiveness of the proposed technique for registering multispectral satellite images.

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.

Imbalanced classification exists commonly in practical applications, and it has always been a challenging issue. Traditional classification methods have poor performance on imbalanced data, especially, on the minority class. However, the minority class is usually of our interest, and its misclassification cost is higher. The critical factor is the intrinsic complicated distribution characteristics in imbalanced data itself. Resampling ensemble learning achieves promising results and is a research focus recently. However, some resampling ensembles do not consider complicated distribution characteristics, thus limiting the performance improvement. In this paper, a generalized optimization embedded framework (GOEF) is proposed based on undersampling bagging. The GOEF aims to pay more attention to the learning of local regions to handle the complicated distribution characteristics. Specifically, the GOEF utilizes out-of-bag data to explore heterogeneous local areas and chooses misclassified examples to optimize base classifiers. The optimization can focus on a single class or both classes. Extensive experiments over synthetic and real datasets demonstrate that GOEF with the minority class optimization performs the best in terms of AUC, G-mean, and sensitivity, compared with five resampling ensemble methods.

Medical image data, like most patient information, has high requirements for privacy and confidentiality. To improve the security of medical image transmission within the open network, we proposed a medical image key area protection algorithm based on reversible data hiding. First, the coefficient of variation is used to identify the key area, that is, the lesion area of the image. Then, the other regions are divided into blocks to analyze the texture complexity. Next, we propose a new reversible data hiding algorithm, which embeds the content of the key area into the high-texture regions. On this basis, a quick response (QR) code is generated using the ciphertext of the basic image information to replace the original lesion area. Experimental results show that this method can not only safely transmit sensitive patient information by hiding the content of the lesion, it can also store copyright information through QR code and achieve accurate image retrieval.

Exponential moments (EMs) are important radial orthogonal moments, which have good image description ability and have less information redundancy compared with other orthogonal moments. Therefore, it has been used in various fields of image processing in recent years. However, EMs can only take integer order, which limits their reconstruction and antinoising attack performances. The promotion of fractional-order exponential moments (FrEMs) effectively alleviates the numerical instability problem of EMs; however, the numerical integration errors generated by the traditional calculation methods of FrEMs still affect the accuracy of FrEMs. Therefore, the Gaussian numerical integration (GNI) is used in this paper to propose an accurate calculation method of FrEMs, which effectively alleviates the numerical integration error. Extensive experiments are carried out in this paper to prove that the GNI method can significantly improve the performance of FrEMs in many aspects.

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.

The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.

This paper proposes a new medical image super-resolution (SR) network, namely deep multi-scale network (DMSN), in the uniform discrete curvelet transform (UDCT) domain. DMSN is made up of a set of cascaded multi-scale fushion (MSF) blocks. In each MSF block, we use convolution kernels of different sizes to adaptively detect the local multi-scale feature, and then local residual learning (LRL) is used to learn effective feature from preceding MSF block and current multi-scale features. After obtaining multi-scale features of different MSF block, we use global feature fusion (GFF) to jointly and adaptively learn global hierarchical features in a holistic manner. Finally, compared with other prediction methods in spatial domain, we applied DMSN in UDCT domain, which enables a better representation of global topological structure and local texture detail of HR images. DM-SN shows superior performance over other state-of-the-art medical image SR methods.

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, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.

This paper presents a new reversible watermarking algorithm for color image. The traditional technique is to embed data into each color channel independently. Considering that the color channels correlate with each other, we propose a reversible watermarking algorithm based on prediction-error expansion that can enhance the prediction accuracy in one color channel through exploiting the gradient information from the other channels. In doing so, the magnitude of the prediction-error is decreased generally, and consequently, the distortion to the host image is diminished. Experimental results demonstrate that the proposed algorithm outperforms the traditional methods that independently embed watermark into each channel.

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.