A Novel Grayscale Image Steganography Scheme Based on Chaos Encryption and Generative Adversarial Networks
Qi Li, Xingyuan Wang, Xiaoyu Wang, Bin Ma, Chunpeng Wang, Yongjin Xian, Yun-Qing Shi
Abstract
This article proposes a novel grayscale image steganography scheme that is capable of hiding an encrypted secret image into a cover image of the same size. The secret image is encrypted using the chaos encryption technology before being hidden. The encrypted image and cover image are then transformed into the steg image by a convolutional neural network (CNN). Also, a generative adversarial network (GAN) is adopted to produce a more realistic steg image, whose appearance is difficult to distinguish from the cover image. The steganography scheme is composed of three CNNs which are regarded as hiding network, discriminative network, and extracting network, respectively. Additionally, a new weight allocation mechanism is introduced to ensure the balanced training procedure of hiding-extracting networks. The obtained experimental results show that the proposed scheme not only solved the problems of secret information leakage and color distortion of the current mainstream methods but also maintain high embedding capacity. Compared with other methods, the average values of the PSNR and SSIM of our steg images can reach 0.987 and 42.3, respectively. And the quality of the reconstructed secret images is also advantageous.