Image camouflage based on Generate Model

Xintao Duan, Haoxian Song, En Zhang, Jingjing Liu

Published 2017-10-21Version 1

To protect image contents, most existing encryption algorithms are designed to transform an original image into a texture-like or noise-like image which is, however, an obvious visual sign indicating the presence of an encrypted image and thus results in a significantly large number of attacks. To address this problem, in this paper, we propose a new image encryption concept to transmit a meaningful normal image that is independent of the original image to the corresponding well-trained generation model to produce the same image as the original image, which is independent of the original image instead of passing the original image and having the same effect as the original image, to achieve the effect of disguising the original image. This image camouflage method not only solves the problem of obvious visual implication, but also guarantees the security of the information.