Visual security index combining superpixel segmentation and block variance calculation for selective encrypted images: Visual security index combining superpixel segmentation

Selective encryption algorithms have currently become an important method for protecting image privacy. Visual security evaluation of selective encrypted images plays an important role in measuring the effectiveness of these algorithms, yet such studies are scarce. In this paper, we propose a visual security index combining superpixel segmentation and block variance calculation for selective encrypted images (SBVSI). Specifically, we propose a method based on superpixel segmentation and block variance calculation to find blocks of pixels that include valid information. These blocks can better represent the image regions of interest to the human visual system, thereby improving the evaluation accuracy. Next, to obtain features with stability, global features and local features of the image are extracted to represent the overall changes in the encrypted image. After that, the global similarity index and local similarity index are constructed using the above two features. Finally, the support vector regression model is used to integrate the similarity indices of global and local features, which effectively combines the information of different features and improves the accuracy and robustness of the visual security assessment. Experimental results on two public selective encrypted databases demonstrate that compared with existing state-of-the-art work, the proposed SBVSI exhibits better performance, especially in handling middle and high quality images.