基于混沌映射和可逆神经网络的图像隐写算法研究OA

To address the problem of image quality degradation caused by large-scale steganography,a new reversible steganography network is designed in this paper.This network combines chaotic mapping and invertible neural net-work,greatly improving the security of steganography while ensuring large-scale capacity.Firstly,a four-dimen-sional chaotic system with line balance is designed.The contents of the secret image are scrambled using a chaotic mapping encryption algorithm to prevent secret image information leakage during transmission.In addition,the cov-er image is preprocessed to hide the secret image in the Y channel,significantly enhancing the steganographic ca-pacity.Extensive experiments have been conducted on the DIV2K and COCO datasets.The peak signal-to-noise ra-tio(PSNR)of the cover image and stego image is as high as41.63 dB,and that of the secret image and recovered image is 43.29 dB.The experimental results demonstrate that the proposed method not only maintains excellent steg-anographic performance under large capacity steganographic conditions but also produces steganographic images with high fidelity.Furthermore,its anti-steganographic capability is far superior to existing advanced algorithms.