Pixel-Decimation-Assisted Steganalysis of Synchronize-Embedding-Changes Steganography

Shunquan Tan, Haojie Zhang, Bin Li, Jiwu Huang

Abstract: This paper deals with the state-of-the-art synchronize-embedding-changes (SECs) steganography. We propose the pixel-decimation-assisted steganalytic feature set, a novel feature set construction protocol that extends upon the recent selection-channel-aware spatial rich model maxSRMd2. Our method is based on pixel decimation, a specific type of image downsampling. Based on theoretical analysis and empirical evaluation, we clearly demonstrate that our method impairs the synchronization of embedding changes in SEC steganography, and improves the accuracy of embedding change probability estimation. Our method significantly improves stego image detection performance when extended from a selection-channel-aware rich-model feature set (maxSRMd2) and is robust to different image downsampling methods. Furthermore, increasing the number of sweeps in SEC steganography has no effect to the performance of our proposed method even though it further strengthens synchronization of embedding changes. It is worth noting that with ensemble classifier, the above-mentioned performance improvements are achieved at a little extra cost.