Multilevel Strong Auxiliary Network for Enhancing Feature Representation to Protect Secret Images

Abstract

Image data play an important role in the network information, however, some images containing sensitive or confidential information are easy to attract the attention of malicious attackers. Based on deep learning and data hiding technology, a novel hiding-revealing network is designed to protect these secret images. The sender uses the hiding network to conceal the secret image into an ordinary cover image and the receiver uses the revealing network to recover the secret image. Symmetrical shortcut connection is designed to improve both the hiding and the revealing performance without adding any parameters. Consider that some secret images may have complex spatial features, a multi-level strong auxiliary module is designed to enhance feature representation and boost the restoration quality of the secret image. Then, a lifeline is proposed to transform the image hiding task into a residual identity mapping, which reduces the difficulty of network learning and obviously improves the hiding performance. In addition, a mixed loss function is designed to further improve the perceptual quality of both the hidden image and the revealed image, which further completely eliminates the secret content in the residual image and ensures the hiding security. Experimental results demonstrate that, compared with the state-of-the-art methods, our proposed method achieves the best performance in both hidden image synthesis and secret image restoration.