Secure Steganography Based on Wasserstein Generative Adversarial Networks with Gradient Penalty

In the field of digital communications, ensuring the security of sensitive data, especially within images, is of paramount importance. This paper introduces the Steganographic Generative Adversarial Network based on Wasserstein Generative Adversarial Networks with Gradient Penalty(SWGAN-GP). The model comprises three networks: the generator that creates realistic cover images, the discriminator that enhances the authenticity of steganography by comparing steganographed and real images, and the steganalysis model that detects steganographic activities in images. By observing the convergence of the loss function of generator, we found that introducing a gradient penalty into the interpolation between real and steganographed images not only strengthens the model's training stability but also accelerates the training convergence. Additionally, the experimental results demonstrate that our model maintain the best image quality and structural integrity after steganography. These findings suggest the great potential of our model in the realm of secure steganographic communications.