Design of a integrated steganalysis model for adversarial steganography

In addition, the model is based on MLP (Multi Layer Perceptron) to construct a deviation sample recognition module, effectively suppressing the negative transfer phenomenon that occurs during the training process, stabilizing the domain distribution alignment process, and further improving the model's cross domain generalization ability in adversarial disturbance environments. The experimental results showed that under different embedding rates and intensities of adversarial steganalysis attacks, the proposed fusion steganalysis model had an average decrease of 15.95% and 6.06% in error rate [M2] (Probability of Error, Pe) compared to the traditional steganalysis models SPAM (Subtractive Pixel Adjacency Matrix steganalysis) and SRM [M1], and an average decrease of 10.93% to 19.50% in error rate compared to deep learning steganalysis models (Ye Net, SRNet, LWENet).

Compared to the robustness enhancement method KDNFT (K-times Dropout Neighboring Feature Transformer) [M3] for adversarial steganalysis, the error rate decreased by an average of 5.90%. Achieve the steganalysis performance of the current SOTA in adversarial steganography scenarios. Conclusion: The fusion based steganalysis framework proposed in this article can effectively reduce the comprehensive error rate of detecting adversarial sample steganographic images, providing a new feasible path for achieving more accurate and highly reliable steganalysis models.