Zero-Shot Interpretable Image Steganalysis for Invertible Image Hiding

2026-05-02

AI Summary

This work addresses the limitations of existing image steganalysis methods, which are predominantly confined to binary classification and rely on the assumption that training and test data share identical distributions, thereby struggling to detect reversible image hiding in real-world scenarios. To overcome these challenges, we propose the first zero-shot, interpretable steganalysis framework specifically designed for reversible image hiding. Our approach unifies hiding, recovery, and detection within a single architecture and incorporates a residual enhancement strategy to improve generalization across diverse datasets and model architectures. Notably, the method requires no target-domain training data and retains the capability to recover hidden messages. Extensive experiments on multiple benchmark datasets demonstrate its superior performance over state-of-the-art methods, confirming both its effectiveness and strong generalization ability.

Abstract

Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security of steganography methods, especially the emerging invertible image hiding approaches. However, prior studies merely classify input images into two categories (i.e., stego or cover) and typically conduct steganalysis under the constraint that training and testing data must follow similar distribution, thereby hindering their application in real-world scenarios. To overcome these shortcomings, we propose a novel interpretable image steganalysis framework tailored for invertible image hiding schemes under a challenging zero-shot setting. Specifically, we integrate image hiding, revealing, and steganalysis into a unified framework, endowing the steganalysis component with the capability to recover the secret information embedded in stego images. Additionally, we elaborate a simple yet effective residual augmentation strategy for generating stego images to further enhance the generalizability of the steganalyzer in cross-dataset and cross-architecture scenarios. Extensive experiments on benchmark datasets demonstrate that our proposed approach significantly outperforms the existing steganalysis techniques for invertible image hiding schemes.

Problem

• Zero-Shot
• Interpretable
• Image Steganalysis
• Invertible Image Hiding
• Cross-Dataset Generalization

Innovation

• zero-shot steganalysis
• invertible image hiding
• interpretable framework
• residual augmentation
• cross-dataset generalization