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ACGAN based coverless image steganography method

Aiming at the problems of low load of traditional image steganography, obvious traces of information embedding, and difficulty in carrier selection, this paper proposes a carrier free steganography method based on Auxiliary Classifier Generative Adversarial Network (ACGAN). Based on the generative adversarial network, ACGAN converts the secret information into noise and inputs it into the generator together with the category label to generate a secret image with controllable content, which belongs to the category of vector synthesis and can effectively avoid the threat of steganalysis. At the same time, the image quality and information embedding capacity of the generated dense images are also significantly improved compared with previous methods. We conducted experiments on the MNIST data set. The experimental results show that this method can complete the functions of dense image generation and message extraction. While generating controllable dense images, the accuracy of message recovery has reached 94%. Verification The security advantages and feasibility of the scheme are discussed.