Ensemble Stego Selection for Enhancing Image Steganography

Abstract

In this paper, we propose an enhancing steganographic scheme by random generation and ensemble stego selection. Different from existing steganography that only focuses on distortion function designing, our scheme considers both distortion model and optimized stego generation. In specific, for given cover, we firstly train an universal steganalyzer to calculate its gradient map, which is referenced to randomly adjust cost distribution of this cover. Multiple candidate stegos are sequentially generated by combining adjusted cost and syndrome trellis coding. Furthermore, we build an ensemble selection mechanism to effectively determine the candidate that is closest to the statistical characteristics of cover image as the final steganographic image. Comprehensive experiments demonstrate that compared to existing state-of-the-art schemes, our scheme can significantly boost the anti-steganalysis capability.