Zhihua Gan

The popularity of wearable medical data collection and surveillance devices provides real-time guarantees for the whole process of a patient's medical treatment, especially medical image data plays a key role. However, existing medical images face data leakage, pollution and vulnerability to attacks during transmission over wireless body area networks(WBANs). To address these issues, a privacy protection algorithm based on Hopfield cross neural network (HCNN) for medical data is proposed. Specifically, the HCNN model is first constructed and its dynamic behavior is analyzed, which is suitable for application to image encryption. Then, a confusion method of NZ fractal curve sorting matrix (NZ-FCSM) is designed to achieve good encryption effect. Subsequently, the secret image sharing (SIS) technique based on sharing matrix is introduced to enhance the algorithm robustness. Finally, an alignment embedding of double diamond prediction (AEDDP) method is proposed to implement lossless hiding of private information. The present issues in medical image protection include ensuring the security and effectiveness of encryption algorithms while maintaining the robustness and concealment of ciphertext data, and balancing the need for preservation with the limited resources of complex work environment. Experimental results show that the proposed algorithm achieves PSNR of 53 dB for the cipher image, more than 36 dB for the reconstructed image, and the information entropy of the secret image is over 7.99, and displays good robustness. These findings highlight the validity of the algorithm in medical image data privacy preserving applications that ensure confidentiality and extend to practical applications of concealed transmission of confidential information and secure multi-party transactions.

Adversarial example detection plays an important role in defending deep neural networks (DNNs) from assaults by distinguishing whether the inputs are benign or manipulated. However, current detection algorithms leave much to be improved in terms of accuracy and stability for high-resolution and slightly perturbation adversarial examples. To address this issue, we propose a multi-information interaction enhancement model based on pixel-feature space for adversarial example detection. Specifically, we design the enhancement module based on statistical information and FFT to extract and enhance feature activation, which contains the most direct evidence for discriminating adversarial perturbations. Besides, we interactively fuse the pixel stream and feature stream data over the channel to obtain dual stream data. Eventually, we devise and add channel variance-pooling in the spatial attention unit to further improve the model’s search for adversarial perturbations. To fully validate the performance of the model, we conduct extensive experiments and analyses on five types of mainstream adversarial examples from the CIFAR-10 and ImageNet datasets. Average F1 scores of 0.95 and 0.91 are achieved on the two datasets, respectively, which proves that the proposed method can accurately and stably detect multiple adversarial examples and effectively alleviate the negative impact of adversarial attacks on DNNs.

Traditional JPEG image encryption that prioritizes solely confidentiality fails to account for the pressing usability requirements of cloud-based environments, thus boosting the boom in thumbnail-preserving encryption (TPE) to balance image privacy and usability. However, existing TPE schemes for JPEG images face numerous challenges, such as insufficient security, inability to achieve lossless decryption, and high file extension. To address these challenges, we propose a TPE scheme based on dynamic M-ary decomposition and adaptive threshold constraints (TPE-MDTC). First, the valid ranges of quantized DC coefficients for JPEG images are determined. Then, a sum-preserving encryption method for quantized DC coefficients with compliance threshold constraints is designed using the bit-plane permutation to preserve thumbnails with high accuracy. Next, the introduction of dynamic M-ary decomposition effectively changes bit statistical characteristics preserved by bit-plane permutation, enhancing the ciphertext security. Finally, a quantized AC encryption method with RV (Run/Value) pair global permutation is proposed, effectively modifying the unit block features, thereby significantly improving the security and attack resistance of encrypted images. Experimental results show that the proposed TPE-MDTC scheme can reconstruct the original JPEG images without loss, and the generated ciphertext images exhibit significant advantages over previous schemes regarding file extension and security.

With the rise of meta-universe, the application of 3D models in various fields has garnered significant attention, for they may furnish more vivid and detailed representations. However, many problems lie in securing the transmission of 3D model data, as the tampered possibility increases while in transit. Therefore, protecting 3D model data is crucial. Meanwhile, existing chaotic systems have many drawbacks such as degeneracy, predictability, and initial value insensitivity, resulting in a high risk of attacks on 3D model encryption algorithms. This study proposes a 3D model encryption algorithm using a 4D non-degenerate chaotic system and adaptive diffusion based on the finite field. Firstly, a 4-dimensional (4D) non-degenerate chaotic system is designed for generating pseudorandom sequences. Compared to the classical 4D chaotic systems, our proposed chaotic system exhibits better chaotic performance in several aspects, such as Lyapunov exponents, NIST test, and initial value sensitivity. Further, the 3D data format of the 3D model is transformed into a 2D data format. Then, a rotation misalignment across rows and columns (RMARC) is presented to confuse the 2D coordinate data format and break the high correlation of the 2D coordinate values. Finally, the scrambled data is diffused within a finite field using the chaotic sequence to get the diffused 3D model. In addition, we have done many experiments to verify the encryption efficiency, security performance, and encryption quality. The experimental results show that the 3D encryption system performs well in these aspects.

In recent years, the Metaverse has garnered significant attention in social and Metahuman realms, showcasing substantial value and immense developmental potential through its integration of virtual and real worlds. However, this integration has also raised security concerns. For instance, in digital image forensics, the malicious dissemination of false images by wrongdoers could result in serious consequences and the propagation of misinformation. This article presented a novel two-branch network (abbreviated as CAFTB-Net) to detect and localize the forged regions of document images in the Metaverse. One branch extracts manipulation trace directly from spatial information, e.g., unnatural smears, anomalies between pixels. The other branch employs an SRM filter to transform the input image from the color domain into the noise domain, effectively extracting anomalies, such as global noise inconsistencies from the noise domain. Compared to spatial domain features, the discontinuity of forgery traces within the noise domain aids the network in authenticating the document image. The two branches extract local and global features in the forged document images. Finally, we propose a cross-attention module to fuse local spatial features and global noise features. Extensive experimental results demonstrate that the proposed network achieves F1 scores of 0.819 and 0.948 on the SACP and ICDAR datasets, respectively, with AUC scores of 0.933 and 0.764, outperforming some of the state-of-the-art algorithms.