Blur region detection from a single image with spatially-varying blur is a challenging task. Although many methods are proposed in the past decades, most of them are based on hand-crafted features. These features are not robust to image context, image size, blur type and other factors, which cannot obtain sound performance. In addition, the craft of these features requires a lot of domain knowledge. To address these problems, in this paper, a blur region detection method based on semantic segmentation is proposed to extract blur regions, which well integrates global image-level context and cross-layer context information making the auto-learned features more robust. Specifically, we design a blur detection net (BDNet) for blur detection by combining ResNets and FCNs. A binary mask can be produced in an end-to-end way. By our method, the mean region intersection over union (Mean IoU) increased by nearly 20% compared with most other blur detection methods. We make the code publicly available at https://github.com/ SEU-DongHan/BDNet.
Abstract:The risk problem of access control model is always the key point of security management. In this paper, we introduce the risk factor to control the risk of role under the proposed threshold when building RBAC policy based on role mining. At the same time, a user should meet some demands to activate roles. So we propose the following principal. A user could activate a role when his trust is higher than the trust threshold of the candidate role. This method makes role authority more reasonable and it avoids of the abuse of permissions. Experiments on performance indicate that the proposed algorithm can not only build the complete RBAC system to avoid roles' high risk caused by the high complexity of permissions in roles, but also meet the least privilege when a user activates a role.
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