Toward high imperceptibility deep JPEG steganography based on sparse adversarial attack

Abstract

JPEG steganography is an important branch of information hiding. However, with the development of deep learning-based steganalysis models, steganography is facing great challenges. To better resist these steganalysis models, this paper proposes a deep JPEG steganography framework based on sparse adversarial attacks. According to the vulnerability of deep steganalysis models to adversarial attacks, sparse adversarial attacks are introduced into the deep JPEG steganographic structure to improve the security of stego images. In addition, to resist steganalysis from JPEG and spatial domains, both JPEG and spatial domain steganalysis models are involved in adversarial training. Finally, to further enhance the imperceptibility of adversarial stego images, the visual perception loss is designed from the perspective of human eyes. Experimental results indicate that compared with the existing methods, the proposed method has higher imperceptibility and security and can resist modern deep steganalysis models in both JPEG and spatial domains to a certain extent. The source code is available at https://github.com/imagecbj/SAE-JS.