Generative Steganography Based on GANs

Traditional steganography algorithms embed secret information by modifying the content of the images, which makes it difficult to fundamentally resist the detection of statistically based steganalysis algorithms. To solve this problem, we propose a novel generative steganography method based on generative adversarial networks. First, we represent the class labels of generative adversarial networks in binary code. Second, we encode the secret information into binary code. Then, we replace the labels with the secret information as the driver to generate the encrypted image for transmission. Finally, we use the auxiliary classifier to extract the label of the encrypted image and obtain the secret information through decoding. Experimental results and analysis show that our method ensures good performance in terms of steganographic capacity, anti-steganalysis and security.