PRNet: A Progressive Recovery Network for Revealing Perceptually Encrypted Images

Perceptual encryption is an efficient way of protecting image content by only selectively encrypting a portion of significant data in plain images. Existing security analysis of perceptual encryption usually resorts to traditional cryptanalysis techniques, which require heavy manual work and strict prior knowledge of encryption schemes. In this paper, we introduce a new end-to-end method of analyzing the visual security of perceptually encrypted images, without any manual work or knowing any prior knowledge of the encryption scheme. Specifically, by leveraging convolutional neural networks (CNNs), we propose a progressive recovery network (PRNet) to recover visual content from perceptually encrypted images. Our PRNet is stacked with several dense attention recovery blocks (DARBs), where each DARB contains two branches: feature extraction branch and image recovery branch. These two branches cooperate to rehabilitate more detailed visual information and generate efficient feature representation via densely connected structure and dual-saliency mechanism. We conduct extensive experiments to demonstrate that PRNet works on different perceptual encryption schemes with different settings, and the results show that PRNet significantly outperforms the state-of-the-art CNN-based image restoration methods.