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Android utilizes a security mechanism that requires apps to request permission for accessing sensitive user data, e.g., contacts and SMSs, or certain system features, e.g., camera and Internet access. However, Android apps tend to be overprivileged, i.e., they often request more permissions than necessary. This raises the security problem of overprivilege. To alleviate the overprivilege problem, this paper proposes MPDroid, an approach that combines static analysis and collaborative filtering to identify the minimum permissions for an Android app based on its app description and API usage. Given an app, MPDroid first employs collaborative filtering to identify the initial minimum permissions for the app. Then, through static analysis, the final minimum permissions that an app really needs are identified. Finally, it evaluates the overprivilege risk by inspecting the apps extra privileges, i.e., the unnecessary permissions requested by the app. Experiments are conducted on 16,343 popular apps collected from Google Play. The results show that MPDroid outperforms the state-of-the-art approach significantly.

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The hyperspectral pansharpening is a significant preprocessing technology in hyperspectral images application. A new optimized injection model-based hyperspectral pansharpening algorithm is proposed in this paper. Compared with the traditional pansharpening methods, the algorithm achieves two major improvements: 1) the total injected spatial information is obtained by integrating the spatial components of hyperspectral (HS) and panchromatic (PAN) images by PCA transformation; and 2) the gain matrix proposed in this paper is composed of two factors which constraint spectral and spatial distortions respectively. Specifically, the morphological open-closing operation and Laplacian of Gaussian enhancement scheme are used for denoising the interpolated HS and PAN images, respectively. Then, the spatial components of the denoised HS and PAN images are respectively extracted by the morphological gradient operation and homomorphic filtering. The PCA transform is applied to the results to obtain the first principal component served as total spatial details. The total spatial information weighted by the gain matrix is finally combined with the interpolated HS images to generate the pan-sharpened images, in which a new gain matrix is constructed to minimize the spectral and spatial distortions. The extensive experiments have demonstrated the potential of the proposed method in balancing spectral preservation and spatial sharpness. INDEX TERMS Hyperspectral pansharpening, hyperspectral image, panchromatic image, spatial information, gain matrix.

A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error.