Hide and Recognize Your Privacy Image

Abstract

Recent studies have demonstrated that deep neural networks show excellent performance in information hiding. Considering the tremendous progress that deep learning has made in image recognition, we explore whether neural networks can recognize invisible private images hidden in cover images. In this article, we propose a method for image recognition in the covert domain using neural networks. Our target is to hide an image inside another image with minimal visual quality loss, while at the same time, the hidden image can be recognized correctly without being recovered. In the proposed system, the hiding and recognition of secret images are all performed by neural networks. The hiding network and the recognition network are designed to specifically work as a pair. We design and jointly train preparation, hiding, and recognition networks, where given a cover and a secret image, the preparation network reduces redundant information of the secret image, the hiding network produces a stego image that is visually indistinguishable from the cover image, and the PSNR and SSIM reach 38.5 dB and 0.991 on the MNIST & CIFAR-10 dataset and 41.8 dB and 0.995 on the CelebA & Scene dataset, respectively. The recognition network can correctly identify the secret image inside the stego image which reaches 98.3% recognition accuracy on MNIST dataset and 91.6% recognition accuracy on CelebA dataset in the covert domain, less than 1% recognition decrease compared with direct recognition. In summary, our approach can successfully identify the secret image without revealing its content. Across various datasets, both the classification accuracy and the invisibility of private images are consistently satisfactory.