A Generative Method for Steganography by Cover Synthesis with Auxiliary Semantics

Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity, it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS) attempts to construct a natural stego image, so that the cover image is not modified; thus, it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images, the development of SCS is limited.In this paper, a novel generative SCS method based on a Generative Adversarial Network(GAN) for image steganography is proposed.In our method, we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN) to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information, category labels can generate pseudo-real images via the generative model.Then, the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST, CIFAR-10, and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security, capacity, and robustness of the method are analyzed.