Image Adversarial Steganography Based on Joint Distortion

Image steganography is the technique of concealing secret messages into digital images without arousing suspicion from detectors. Recently, adversarial steganography has received much attention from the research community, since it is effective in deceiving target deep-learning-based steganalysis (DLS) and designing more secure embedding distortion. However, how to combine adversarial steganography with handcrafted adjustment strategies to design adversarial steganography based on joint distortion has not been discussed yet. In this paper, incorporating adversarial steganography and joint distortion assignment, we present a novel adversarial steganographic scheme named JAS (Joint Adversarial Steganography). We compute joint distortion and adjust it based on joint gradient, which is a vector consisting of the gradients of two adjacent pixels, until the resulting stego image could deceive the target DLS. Furthermore, by combining JAS with synchronizing modification directions profile, we enhance the steganography security more desirably. Experiments demonstrate that the proposed method effectively enhances the anti-detection ability of joint distortion steganography.