Techniques for Improving Visual Security and Restoring Visibility

Visual data security is ensuring that information cannot be seen by unauthorized individuals. This is particularly important when dealing with private or sensitive information, and the threat of a breach has risen enormously with the shift in working practices towards increased mobility, flexibility and shared resources. In the development in recent decades of various efficient image encryption algorithms, such as selective encryption, a great demand has arisen for methods of examining the visual security of encrypted images. Existing solutions usually adopt well-known metrics to measure the quality of encrypted images, but they often show undesired behavior on perceptually encrypted images of low quality. In this paper, we propose a novel visual security index (VSI) based on the human visual system. The proposed VSI evaluates five aspects of the content similarity between plain and encrypted images: the edge similarity extracted via multi-threshold edge detection, the texture similarity measured by means of the co-occurrence matrix, frequency analysis by means of DWT, statistical analysis by means of histogram and intensity by means of pulse amplitude. These five components are further integrated to obtain the proposed VSI through adaptive similarity weighting. Then visibility restoration techniques are used to regain the visibility of the image.