Constructing An Intrinsically Robust Steganalyzer via Learning Neighboring Feature Relationships and Self-adversarial Adjustment

The effectiveness of deep learning-based steganalyzers is significantly compromised by adversarial steganography. In response to this challenge, recent efforts have been devoted to identifying distinct traces of adversarial perturbations, yet they have overlooked the inherently adversarial robustness required in steganalyzers. This paper aims to develop a steganalytic model that defends against adversarial steganography by increasing the difficulty of generating adversarial stego images. To achieve this objective, the techniques of learning neighboring feature relationships and self-adversarial adjustment are proposed with three essential modules. The first one, named K-times Dropout Neighboring Feature Transformer (KDNFT), is designed to accept a set of neighboring features obtained by dropout as input. Based on the finding that K-times dropout neighboring features have different distributions for covers and adversarial stegos, KDNFT effectively learns to exploit the relationships among these features for adversarial steganalysis. To facilitate adversarial training, which is an effective way to improve intrinsic robustness, the second module called Pseudo Adversarial Stego Generator (PASG) is proposed to synthesize samples for training. The third module is a Test-time Active Perturbation (TAP) module that adjusts the results of adversarial stego samples close to the decision boundary in a self-adversarial way. Extensive experiments demonstrate that our method achieves improvements in steganalyzing various kinds of adversarial steganographic methods.