Lightweight Steganalysis With Low Distortion for Countering Robust Image Steganography

Abstract

Robust image steganography in social networks presents significant challenges for steganalysis. Existing methods exploit low payload visibility and resistance to evade conventional passive steganalysis, leading to high false-positive and missed detection rates, which undermines reliable verification and poses a significant security threat to cyberspace. Active steganalysis is designed to eliminate hidden information through controlled modifications. However, existing methods usually encounter challenges such as a decline in the visual quality of processed images, excessive training costs of the models, and dependence on the image size. All these have limited the practical application of active steganalysis. To address this issue, we propose a block-based multi-objective analysis framework, which integrates dual-block constraints with multi-objective loss optimization. This framework achieves adaptive image partitioning by dividing the image into non-overlapping $2\times 2$ pixel blocks. The partitioning simultaneously satisfies distortion boundary constraints and luminance preservation requirements. A multi-objective loss function is proposed to eliminate redundant information effectively while maintaining image visual clarity. The weights of loss function are set based on evolutionary optimization. Furthermore, the performance of the proposed method is enhanced by a linear-time complexity depth-first search algorithm, which minimizes the PSB loss under block-level constraints. The optimization process incorporates perturbation-based refinement to facilitate global convergence. Extensive experiments conducted on the BOSSBase-1.01 and CelebA datasets validate the effectiveness of the proposed framework in defending against robust image steganography prevalent in social networks. Evaluations across different payload settings indicate that the proposed method achieves an average PSNR improvement of approximately 34% over AO-Net across both BOSSBase-1.01 and CelebA datasets. Furthermore, the framework processes standard test images with a latency of 6.4ms, without requiring any preset image size.