Directional Adversarial Noise-Based Universal Steganalysis Method for Detecting Adversarial Steganography

Adversarial steganography presents a significant challenge in digital media security, leveraging adversarial perturbations to obscure steganographic patterns and evade detection by traditional steganalysis methods. This paper proposes a universal detection method based on Directional Adversarial Noise Search (DANS) to improve the robustness of steganalysis against adversarial steganography. Specifically, an augmentation network is employed to generate adversarial noise distributions, guided by a novel DANS loss function that enables precise optimization of noise distribution. A threshold constraint is further applied to ensure the controllability of the generated noise. By embedding the generated perturbations into both cover and stego images, a robust augmented dataset is constructed to improve detection performance. Experimental results demonstrate that the proposed method improves detection accuracy across various adversarial steganography techniques, achieving up to a 17.14% increase. Moreover, it exhibits superior generalization ability in cross-dataset evaluations, highlighting its effectiveness and robustness in diverse scenarios.