A Secure Video Compression Mechanism Based on Chaos Cryptography and Deep Learning Network
Kai Li, Jiao Wan, Zhiwei Xiang, Meihui Hu, Jinping Cao · 2024
Many video image compression methods overlook data encryption, exposing original motion information and risking information leakage. Existing schemes prioritize compression efficiency, neglecting image data integrity protection. This paper introduces a secure video image compression technology leveraging chaos cryptography and deep learning. The optical flow method calculates pixel differences between adjacent frames, inputting them into a deep learning encoder network for compression. The compressed image is then scrambled and encrypted using a sequence from the chaos cryptographic algorithm. This approach enhances security and confidentiality during compression and transmission, with improved detection capability against pixel replacement attacks. Experimental results demonstrate superior performance over the H.265 standard in terms of PSNR and BPP indicators.