Image steganography based on wavelet transform and Generative Adversarial Networks

Abstract

For most steganography based on GANs, repeated encoding and decoding operations can easily lead to information loss, making it hampers the generator’s ability to effectively capture essential image features. To address the limitations in the current work, we propose a new generator with U-Net architecture. Introducing the graph network part to process the information of graph structure, and introducing a feature transfer module designed to preserve and transfer critical feature information. In addition, a new generator loss structure is proposed, it contains three parts: the adversarial loss $l_G^1$, which significantly enhances resistance to detection, the entropy loss $l_G^2$, which ensures the embedding capability of steganographic images, and the low-frequency wavelet loss $l_f$, which optimizes the overall steganographic performance of the images. Through a large number of experiments and comparisons, our proposed method effectively improves the steganography detection ability, and verifies the reasonableness of the proposed method.