Yongjin Xian

With the extensive adoption of cross-chain technology, its application scenarios have become increasingly diversified and sophisticated. However, current cross-chain data exchange solutions are primarily designed for specific blockchain interactions. As a result, unified identity authentication and data privacy protection across heterogeneous blockchain systems remain significant challenges. These challenges are particularly acute in regulatory scenarios, where secure and trustworthy cross-chain oversight is essential. Accordingly, a series of novel solutions is proposed to construct a reliable cross-chain regulatory ecosystem. First, a privacy-preserving regulatory market model is proposed for cross-chain environments, aiming to enable secure, unified, and efficient regulation across heterogeneous blockchains. Second, a key agreement algorithm is designed based on permutation rational functions over peninsula groups, allowing secure and reliable integration of heterogeneous blockchain platforms into the regulatory market. Finally, a privacy-preserving scheme is proposed based on oblivious transmission, whereby regulators are equipped with the ability to discreetly select and audit cross-chain data, thus ensuring privacy throughout the regulatory process. The proposed approach is shown to outperform classical schemes in terms of efficiency. In addition, its robustness against common attack vectors is verified through comprehensive security analysis. Together, these contributions provide theoretical and practical support for the development of a trustworthy cross-chain regulatory ecosystem.

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to generate hash codes, which limit their ability to represent key features of images effectively. This article introduces a perceptual region recognition network (PRRN) to accurately identify key feature regions in images based on their texture distribution characteristics, thereby generating image perceptual hashing codes that reflect the key content of the images. At the same time, a perceptual hashing feature extraction module, which integrates a residual network (ResNet) and a weighted feature fusion network (WFFN), is built to extract deep semantic features of the object image. Where, ResNet is leveraged to extract high-level semantic features, while WFFN ensures the preservation of low-level local features. Furthermore, skip connections are employed to achieve content enhancements for intricate details of critical image regions. Additionally, the mean-squared error (MSE) loss is incorporated to enhance the accuracy of key region localization, further improving the sensitivity of image perceptual hash codes and accelerating the network’s convergence speed. Extensive experimental evaluations demonstrate that the proposed PRRN-based perceptual image hashing scheme significantly outperforms other state-of-the-art methods in terms of image feature representation capability. Specifically, it achieves an average improvement of over 1.2% in attack-resistant capability for images compared with other counterparts, making it a promising candidate for practical applications in the IoT environment.

The proliferation of deepfakes has heightened the necessity to distinguish between authentic and counterfeit faces. While numerous methods concentrate on detecting deepfakes, only a few address safeguarding genuine faces from manipulation. This letter proposes a novel active forensics system for deepfake forensics utilizing separable perceptual hash enhancement algorithm. A separable perceptual hash code specifically designed for face deep forgery is introduced, achieving robustness while maintaining sensitivity and imperceptibility when embedded within the original image. Additionally, a multi-scale perceptual smoothing loss function is employed to optimize perceptual similarity, structural smoothness, and embedding stability. As a result, this system ensures the consistence of confidential information both before and after manipulation, thereby enhancing the capability of face source detection and deepfake identification. Experimental results demonstrate that the proposed scheme can effectively counter traditional deep forgery techniques while exhibiting significant potential in preserving personal privacy.

Existing deep learning-based reversible data hiding (RDH) predictors are affected by the difference of pixel complexity, which leads to the reduction of prediction accuracy. Therefore, this letter proposes an optimization framework tailored for RDH predictors, which integrates the local complexity of pixels into the predictor's regression optimization process. By analyzing the image's texture features, the framework adaptively determines the optimal prediction coefficients, thereby improving prediction accuracy. Notably, this optimization framework is versatile and can be applied to optimize other deep learning-based RDH predictors. Additionally, recognizing the critical role of interpolation strategies in RDH pixel prediction, we introduce a multi-scale fusion-enhanced interpolation network specifically designed for RDH, which integrates features across different scales to provide accurate reference pixels for subsequent predictions. Finally, experimental results demonstrate that the proposed method outperforms several advanced RDH predictors in terms of both prediction accuracy and embedding performance.