Visual Security Evaluation of Perceptually Encrypted Images based on Multi-Task Learning

Abstract

Over past decades, many image encryption algorithms have been proposed, among which we can cite the perceptual/selective encryption methods which have attracted wide attention. Such methods allow for adjusting the scrambling intensity, it is therefore essential to have a reliable visual security metric to adjust the scrambling intensity on the one hand and to evaluate the visual security of encrypted images on the other hand. Usually, these tasks are performed based on classical randomness-based measures or image quality assessment metrics. However, these methods have shown their inadequacy as a visual security metric, as they do not address content intelligibility, which represents an essential security requirement. Moreover, these methods are either dedicated to the prediction of visual security (VS) or visual quality (VQ), but not both. In this paper, we propose a no-reference (NR) visual security metric for perceptually encrypted images based on deep multi-task learning, which we dub the Multi-Task Visual Security (MTVS) metric. The proposed metric consists of one shared convolutional neural network (CNN) followed by two separate sub-networks of fully-connected (FC) layers, where one sub-network is responsible for predicting the VS score, while the other is for predicting the VQ score. Experiments were performed on two publicly perceptually encrypted image databases and the results show that the proposed metric yields superior performance on both VS and VQ prediction tasks. The source code and models are available at: https://github.com/Mamadou-Keita/MTVS.