Applying GMEI-GAN to Generate Meaningful Encrypted Images in Reversible Data Hiding Techniques

Symmetry

Josh Jia-Ching Ying¹  Chwei-Shyong Tsai¹  Yu-Wen Li¹  Hsien-Chu Wu² 

[1] Department of Management Information Systems, National Chung Hsing University, Taichung 402, Taiwan;Language Center, National Chin-Yi University of Technology, Taichung 411, Taiwan;

关键词: reversible data hiding;    symmetric encryption;    generative adversarial networks;    residual learning;    convolutional neural network;   

DOI  :  10.3390/sym13122438

来源: DOAJ

【 摘 要 】

With the rapid development of information technology, the transmission of information has become convenient. In order to prevent the leakage of information, information security should be valued. Therefore, the data hiding technique has become a popular solution. The reversible data hiding technique (RDH) in particular uses symmetric encoding and decoding algorithms to embed the data into the cover carrier. Not only can the secret data be transmitted without being detected and retrieved completely, but the cover carrier also can be recovered without distortion. Moreover, the encryption technique can protect the carrier and the hidden data. However, the encrypted carrier is a form of ciphertext, which has a strong probability to attract the attention of potential attackers. Thus, this paper uses the generative adversarial networks (GAN) to generate meaningful encrypted images for RDH. A four-stage network architecture is designed for the experiment, including the hiding network, the encryption/decryption network, the extractor, and the recovery network. In the hiding network, the secret data are embedded into the cover image through residual learning. In the encryption/decryption network, the cover image is encrypted into a meaningful image, called the marked image, through GMEI-GAN, and then the marked image is restored to the decrypted image via the same architecture. In the extractor, 100% of the secret data are extracted through the residual learning framework, same as the hiding network. Lastly, in the recovery network, the cover image is reconstructed with the decrypted image and the retrieved secret data through the convolutional neural network. The experimental results show that using the PSNR/SSIM as the criteria, the stego image reaches 45.09 dB/0.9936 and the marked image achieves 38.57 dB/0.9654. The proposed method not only increases the embedding capacity but also maintains high image quality in the stego images and marked images.

【 授权许可】

Unknown