Iteratively Generated Adversarial Perturbation for Audio Stego Post-processing

Abstract

Recent studies have shown that adversarial examples can easily deceive neural networks. But how to ensure the accuracy of extraction while introducing perturbations to steganography is a major difficulty. In this paper, we propose a method of iterative adversarial stego post-processing model called IA-SPP that can generate enhanced post-stego audio to resist steganalysis networks and the SPL of adversarial perturbations is restricted. The model decomposes the perturbation to the point level and updates point-wise perturbations iteratively by the large-absolute-gradient-first rule. The enhanced post-stego obtained by adding the stego and the adversarial perturbation has a high probability of being judged as a cover by the target network. In particular, We further considered how to simultaneously fight against multiple networks. The extensive experiments on the TIMIT show that the proposed model generalizes well across different steganography methods.