Image Encryption and Compression Based on Reversed Diffusion Model

Yilin Guo, Jianhui Chang, Yuhuai Zhang, Jian Zhang, Siwei Ma

Abstract: Nowadays, as critical conduits of communication, the information security of images and videos is particularly important. The existing encryption techniques usually transform images into high-frequency content that resembles noise, pre-senting significant challenges in achieving efficient compression. This paper presents an innovative collaborative approach that integrates image encryption and compression using a reversed diffusion model. This method, by reversing the typical process of diffusion models, adeptly changes encrypted high-frequency content into a domain that is more amenable to compression. Leveraging the reversible nature of the Denoising Diffusion Implicit Models (DDIM), our framework ensures the high-fidelity restoration of information. Our experimental findings demonstrate that this approach not only effectively encrypts images but also compresses the encrypted high-frequency noise content, outperforming Video Versatile Coding (VVC) in compression performance.

External IDs:dblp:conf/pcs/GuoCZZM24