Leveraging High-Dimensional Mapping for Effective JPEG Steganalysis

Abstract

Steganography is a critical information-hiding technique widely used for the covert transmission of secret information on social media. In contrast, steganalysis plays a key role in ensuring information security. Although various effective steganalysis algorithms have been proposed, existing studies typically treat color images as three independent channels and do not fully consider robust features suitable for JPEG images. To address this limitation, we propose a robust steganalysis algorithm based on high-dimensional mapping. By analyzing the changes in color images during the JPEG compression and decompression processes, we observe that the embedding of secret information causes shifts in the JPEG coefficients, which subsequently affects feature representation during decompression. Based on this observation, our method captures steganographic traces by utilizing the transformation errors produced during decompression. Additionally, due to the imbalance between luminance and chrominance, the feature weights of each channel are uneven. To ensure balanced analysis across the three channels, we adjust the distribution differences of each channel through high-dimensional mapping, thereby reducing intraclass feature variations. Experimental results demonstrate that the proposed method outperforms existing approaches in most cases.