Enhancing the Security of Deep Learning Steganography via Adversarial Examples
Yueyun Shang, Shunzhi Jiang, Dengpan Ye, Jiaqing Huang
Abstract
Steganography is a collection of techniques for concealing the existence of information by embedding it within a cover. With the development of deep learning, some novel steganography methods have appeared based on the autoencoder or generative adversarial networks. While the deep learning based steganography methods have the advantages of automatic generation and capacity, the security of the algorithm needs to improve. In this paper, we take advantage of the linear behavior of deep learning networks in higher space and propose a novel steganography scheme which enhances the security by adversarial example. The system is trained with different training settings on two datasets. The experiment results show that the proposed scheme could escape from deep learning steganalyzer detection. Besides, the produced stego could extract secret image with less distortion.