Progressive JPEGs in the Wild: Implications for Information Hiding and Forensics

Progressive JPEGs in the Wild: Implications for Information Hiding and Forensics

연구 분야: Strategies

학회: IH&MMSec '23: Proceedings of the 2023 ACM Workshop on Information Hiding and Multimedia Security

JPEG images stored in progressive mode have become more prevalent recently. An estimated 30% of all JPEG images on the most popular websites use progressive mode. Presumably, this surge is caused by the adoption of MozJPEG, an open-source library designed for web publishers. So far, the optimizations used by MozJPEG have not been considered by the multimedia security community, although they are highly relevant. The goal of this paper is to document these optimizations and make them accessible to the research community. Most notably, we find that Trellis optimization in MozJPEG modifies quantized DCT coefficients in order to improve the rate-distortion tradeoff using a perceptual model based on PSNR-HVS. This may compromise the reliability of known methods in steganography, steganalysis, and image forensics when dealing with images compressed with MozJPEG. We also find that the type and order of scans in progressive mode, which MozJPEG adjusts to the image, offer novel cues that can aid forensic source identification.