Spatial Image Steganography Based on Generative Adversarial Network
This addresses the problem of secure data hiding in digital images for applications like covert communication, offering incremental improvements over existing methods.
The paper tackles the challenge of embedding secret information into digital images against deep learning-based steganalysis by proposing a secure steganography algorithm using adversarial training with a generator, embedding simulator, and discriminator. It shows dramatic security performance improvements over ASDL-GAN and better than S-UNIWARD, with training time reduced to 30% of ASDL-GAN.
With the recent development of deep learning on steganalysis, embedding secret information into digital images faces great challenges. In this paper, a secure steganography algorithm by using adversarial training is proposed. The architecture contain three component modules: a generator, an embedding simulator and a discriminator. A generator based on U-NET to translate a cover image into an embedding change probability is proposed. To fit the optimal embedding simulator and propagate the gradient, a function called Tanh-simulator is proposed. As for the discriminator, the selection-channel awareness (SCA) is incorporated to resist the SCA based steganalytic methods. Experimental results have shown that the proposed framework can increase the security performance dramatically over the recently reported method ASDL-GAN, while the training time is only 30% of that used by ASDL-GAN. Furthermore, it also performs better than the hand-crafted steganographic algorithm S-UNIWARD.