A high-capacity image steganography algorithm based on end-to-end deep learning networks

Due to the complex texture, large redundant space, and widespread application of images, image based steganography algorithms are still the mainstream direction of steganography. Deep neural network-based picture steganography algorithms have become a research hotspot in the field of image steganography in recent years due to their increasingly good steganographic performance. This article proposes a high-capacity image steganography algorithm based on a generative adversarial network (GAN). The algorithm designed an information embedding and extraction network with a preprocessing module, a convolutional neural networks (CNN) module, and a U-Net as the main components. Through the constraint of the loss function, the embedding, extraction, and discrimination networks were jointly trained to achieve good steganographic visual effects. The experimental results show that our algorithm achieves an embedding capacity of 24 bpp. The overall superiority of the end-to-end steganography network designed in this paper is demonstrated by the fact that, under the assumption of high-capacity embedding, the encrypted images produced by the algorithm in this paper and the extracted secret images are higher than other comparable algorithms in both subjective visual quality and objective visual indicator peak signal-to-noise ratio (PSNR).