Xianquan Zhang

Content authentication is a significant problem of the research of image security. This article exploits visual and structural feature maps to construct a new image hashing algorithm for content authentication. The visual feature map is extracted by the luminance contrast model. Since the visual feature map can reflect image regions of human visual attention, the features of the tampered areas are captured in the hash. In addition, the structural feature map is extracted by the dual-tree complex wavelet transform. Next, the block Krylov singular value decomposition is applied to the visual feature map and the structural feature map for constructing feature vectors. Finally, feature vector distances are calculated and encrypted to produce hash. Extensive experiments are performed on the public databases to demonstrate effectiveness. The results demonstrate that the proposed algorithm can correctly detect 89.83% similar images and 93.93% forged images under the optimal threshold. The comparative results show that the proposed algorithm outperforms some baseline algorithms in content authentication.

No-Reference Image Quality Assessment (NR-IQA) serves as a fundamental task in computer vision that aims to predict image quality consistent with human perception. Currently, numerous NR-IQA methods often use simplistic fusion strategies to integrate features from different backbones. However, these methods typically neglect the semantic differences and intricate inter-channel interactions among different features, thereby limiting their abilities to represent features effectively. To address this issue, we propose a Semantic-guided Channel Cross-attention Integration Network for NR-IQA (SCCIN-IQA), which enables more effective integration of complementary information from different backbones. The core module of our method is the fusion-semantic channel cross-attention. It first generates a semantic feature by integrating features from different backbones, then utilizes this semantic feature as a query to integrate the original backbone features via a channel cross-attention mechanism, thereby adaptively highlighting quality-relevant channel activations. Additionally, a space-channel enhancement module is introduced to further enhance the learned features in both space and channel dimensions, enabling comprehensive modeling of multi-dimensional contextual dependencies. Extensive experiments conducted on multiple public datasets demonstrate that the proposed SCCIN-IQA achieves state-of-the-art performance, consistently surpassing several mainstream methods while exhibiting strong generalization.

To facilitate cross-platform access and convenient sharing of personal photos, an increasing number of consumers are uploading such content to cloud storage via various electronic devices. However, these practices notably amplify the risk of privacy breaches. Reversible data hiding in encrypted images (RDH-EI) emerges as a promising solution that secures both the original image content and the concealed secret data. This paper proposes a high-capacity RDH-EI method based on pixel prediction and entropy encoding. A key limitation of regression-based predictors is their sensitivity to outliers, which impairs parameter estimation and reduces predictive performance. Nevertheless, most existing predictors lack targeted mechanisms to mitigate this issue. To address this gap, we propose a robust regression-based predictor that effectively suppresses the adverse impact of outliers. We introduce two optimization techniques, prediction error grouping and intra-group transformation, to improve the efficiency of entropy encoding. First, a pre-trained neural network model is exploited to extract multi-channel deep features from the original image. Next, we propose a novel entropy-based channel selection mechanism to identify the most informative channels, which are then fused into a comprehensive feature. Based on this feature, prediction errors are partitioned into blocks, which are then clustered into multiple groups. Intra-group transformations, including non-negative and radix-remainder transformations, are then applied to these groups to concentrate their data distribution. Experimental results demonstrate that the proposed method achieves extremely high embedding rates across diverse test images and benchmark datasets, such as BOSSBase, BOWS2, the BrainTumor medical image dataset, and the USC-SIPI texture image dataset, outperforming a range of state-of-the-art methods.

Secure and high-capacity secret information transmission is an important task of the image hiding research. The existing image hiding methods face some critical issues: cover-based methods offer high capacity but introduce image distortion and security risks, whereas secure coverless methods have low capacity. To address these issues, this paper proposes a novel generative-based coverless multi-image hiding method called GCL-MIH, which can achieve high capacity and high security. The GCL-MIH first utilizes a feature reverse module to compress multiple secret images into multiple feature vectors and then normalizes them to generate a vector that conforms to a standard normal distribution, and finally inputs this vector into an invertible generative network (Flow-GAN) to generate a face image, enabling coverless multiple-image hiding without a predefined cover image. Experimental results demonstrate that the GCL-MIH successfully hides up to four images within a single generated face image, achieving a maximum embedding rate of 32 bpp. This capacity far exceeds those of the existing coverless methods. On the COCO test set, the generated stego images of the GCL-MIH are highly realistic (FID score: 11.98), and the recovered secret images exhibit satisfactory fidelity (the average PSNR and SSIM of four recovered secret images are 33.18 dB and 0.9412).