PRNet: a progressive recovery network for revealing perceptually encrypted images

Xiang, Tao, Yang, Ying, Liu, Hangcheng and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2021. PRNet: a progressive recovery network for revealing perceptually encrypted images. Presented at: ACM Multimedia 2021, Chengdu, China, 20-24 October 2021. Proceedings of the 29th ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery, pp. 3537-3545. 10.1145/3474085.3475517

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Association for Computing Machinery
ISBN:	9781450386517
Last Modified:	06 Aug 2025 14:36
URI:	https://orca.cardiff.ac.uk/id/eprint/142563

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