Steganalytic feature based adversarial embedding for adaptive JPEG steganography

摘要

In this paper, we present a novel adversarial embedding scheme named Steganalytic Feature based Adversarial Embedding (SFAE), which is elaborately designed in a non-data-driven style. Firstly, a novel DCTR based adversary is designed to generate adversarial stego images which can not only resist feature based steganalysis but also deep learning based steganalysis. Specifically, our adversary consists of an end-to-end neural network structure, while its inner weights are set according to DCTR rather than learned from datasets. Secondly, we use the minimum distance to the cover in steganalytic space as the criterion to select the optimal adversarial stego image, rather than fooling the adversary. Last but not least, we present two SFAE implementations to adapt to different cases. One is Iterative SFAE, which needs to calculate gradients multiple times. Iterative SFAE is more secure but has higher complexity. It fits the case that the steganographer has adequate computing resources. Another implementation is Oneshot SFAE, which can calculate gradients once. Oneshot SFAE trades the security for lower complexity. It fits the steganographer that has stricter requirements for running time. Experiments demonstrate that SFAE is effective to improve the security of conventional steganographic schemes against the state-of-the-art steganalysis including both feature based steganalysis and deep learning based steganalysis.