Reversible data hiding in encrypted images (RDH-EI) is instrumental in image privacy protection and data embedding. However, conventional RDH-EI models, involving image providers, data hiders, and receivers, limit the number of data hiders to one, which restricts its applicability in scenarios requiring multiple data embedders. Therefore, the need for an RDH-EI accommodating multiple data hiders, especially for copyright protection, has become crucial. Addressing this, we introduce the application of Pixel Value Order (PVO) technology to encrypted reversible data hiding, combined with the secret image sharing (SIS) scheme. This creates a novel scheme, PVO, Chaotic System, Secret Sharing-based Reversible Data Hiding in Encrypted Image (PCSRDH-EI), which satisfies the $(k,n)$ threshold property. An image is partitioned into N shadow images, and reconstruction is feasible when at least k shadow images are available. This method enables separate data extraction and image decryption. Our scheme combines stream encryption, based on chaotic systems, with secret sharing, underpinned by the Chinese remainder theorem (CRT), ensuring secure secret sharing. Empirical tests show that PCSRDH-EI can reach a maximum embedding rate of 5.706 bpp, outperforming the state-of-the-art and demonstrating superior encryption effects.

The widespread adoption of consumer electronics has enabled patients to easily schedule appointments with doctors through online platforms, advancing the development of telemedicine. Utilizing AI technology, these platforms assist physicians in symptom analysis and providing precise recommendations, overcoming geographical barriers and achieving a balanced utilization of medical resources. Moreover, the advancement of 5G and the anticipated 6G networks has provided the capability for swift and accurate execution of cloud-based AI applications. However, online medical image consultations based on plaintext carry the risk of significant privacy breaches. By leveraging secret sharing technology, privacy leakage issues can be effectively addressed. However, the application of this technology alters the visual representation of image data. In multi-party scenarios, each party possesses only one secret share, which makes verifying the authenticity of the image challenging. To address this issue, we propose the Secure Federated Image Authentication system, which enables the authentication of encrypted images provided by consultants before the provision of services. This system employs technologies such as random feature signatures, secret sharing, and distributed reversible data hiding to achieve federated authentication of images. Through security analysis and performance evaluation, our method has demonstrated satisfactory results in terms of security and availability.

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks.

This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance. To address this issue, colaGAE employs mutual isomorphism as a pretext task for a continuous latent space sampling GAE (colaGAE). The central idea of mutual isomorphism is to sample from multiple views in the latent space and reconstruct the graph structure, with significant improvements in terms of the model’s training difficulty. To investigate whether continuous latent space sampling can enhance GAEs’ learning of graph representations, we provide both theoretical and empirical evidence for the benefits of this pretext task. Theoretically, we prove that mutual isomorphism can offer improvements with respect to the difficulty of model training, leading to better performance. Empirically, we conduct extensive experiments on eight benchmark datasets and achieve four state-of-the-art (SOTA) results; the average accuracy rate experiences a notable enhancement of $0.3\%$, demonstrating the superiority of colaGAE in node classification tasks.