Optimization of Key-Driven Compressive Image Encryption Algorithm Based on Chaos-CNN Hybrid Network

Abstract

Abstract: In this paper, a Critical Driven Compressed Image Encryption Framework (CCE) based on a hybrid structure of chaos-convolutional neural network (CNN) is proposed to solve the problems of traditional concatenation methods in terms of energy efficiency and security. The SHA3-512 hash function is used to extend the user key to generate a dual-channel dynamic key, in which one activates the multi-mode chaotic system to inhibit digital degradation, and the other uses the CNN weight self-adjustment mechanism to construct a nonlinear mapping relationship, which improves the key space by 3 orders of magnitude compared with the traditional method. The Swin Transformer model is introduced to analyze the block-level entropy of the image, and an adaptive matching mechanism of entropy value-quantization parameter-encryption strength is established, which reduces the encryption energy consumption by 37% on the basis of maintaining more than 96% of the local structural integrity. The designed hybrid measurement matrix satisfies the limited equidistant nature of compressive sensing through Tikhonov regularization constraint number and Gram-Schmidt orthogonalization processing, and improves the signal reconstruction accuracy by 23% compared with the traditional Toeplitz matrix. By constructing a ciphertext mutual information obfuscator through the adversarial generation network, the success rate of known plaintext attacks is reduced to 0.4%, and the detection accuracy of selected ciphertext attacks exceeds 98%. Experiments show that the reconstructed PSNR of the framework on the GPU/FPGA/CPU hybrid platform reaches 42.6dB (JPEG 36.4dB), and the system energy efficiency is increased by 58% compared with AES+JPEG, and the contribution of each module is verified by ablation experiments: CNN regularization (29%), entropy adaptive partitioning (34%), and adversarial confusion (37%).