Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters. In addition, the single residual block stacking may pay attention to protecting stego signals and neglect the mining of hidden features. To address these issues, we propose a steganalysis model based on dual-path enhancement and fractal downsampling, which is suitable for both spatial and JPEG domains. The model reuses and strengthens noise residuals through two dual-path enhancement blocks, and designs a fractal downsampling block for downsampling at multiple levels, angles, and composition structures. The experimental results demonstrate that the proposed model achieves the best detection performance in both spatial and JPEG domains compared with other start-of-the-art methods. Besides, we design a series of ablation experiments to verify the rationality of each component.

The power grid has transitioned into a network system, comprising increasingly large-scale, complex multi-intelligent devices, multi-sensors, and multi-control units. Concurrently, the rapid advancement of quantum computers presents significant challenges to cross-domain authentication based on traditional cryptographic systems due to decryption issues. To address the issue of sharing identity authentication information across different trust domains, this paper introduces a lattice-based cross-domain encryption transmission scheme. This scheme resists quantum attacks and facilitates cross-domain identity authentication by pre-storing public parameters in the verifier entity and constructing a quantified trust relationship model between nodes within the cross-domain mechanism. This approach further enhances the security of keys and the authentication efficiency between nodes.