GAN-based image steganography by exploiting transform domain knowledge with deep networks

Abstract

Image steganography secures the transmission of secret information by covering it under routine multimedia transmission. During image generation based on Generative Adversarial Network (GAN), the embedding and recovery of secret bits can rely entirely on deep networks, relieving many manual design efforts. However, existing GAN-based methods always design deep networks by adapting generic deep learning structures to image steganography. These structures themselves lack the feature extraction that is effective for steganography, resulting in the low imperceptibility of these methods. To address the problem, we propose GAN-based image steganography by exploiting transform domain knowledge with deep networks, called EStegTGANs. Different from existing GAN-based methods, we explicitly introduce transform domain knowledge with Discrete Wavelet Transform (DWT) and its inverse (IDWT) in deep networks, ensuring that each network performs with DWT features. Specifically, the encoder embeds secrets and generates stego images with the explicit DWT and IDWT approaches. The decoder recovers secrets and the discriminator evaluates feature distribution with the explicit DWT approach. By utilizing traditional DWT and IDWT approaches, we propose EStegTGAN-coe, which directly adopts the DWT coefficients of pixels for embedding and recovering. To create more feature redundancy for secrets, we extract DWT features from the intermediate features in deep networks for embedding and recovering. We then propose EStegTGAN-DWT with traditional DWT and IDWT approaches. To entirely rely on deep networks without traditional filters, we design the convolutional DWT and IDWT approaches that conduct the same feature transformation on features as traditional approaches. We further replace the traditional approaches in EStegTGAN-DWT with our proposed convolutional approaches. Comprehensive experimental results demonstrate that our proposals significantly improve the imperceptibility and our designed convolutional DWT and IDWT approaches are more effective in distinguishing high-frequency characteristics of images for steganography than traditional DWT and IDWT approaches.