Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies |
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| Affiliation: | 1. School of Information Science and Engineering, Hunan University, Changsha 410082, China;2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China;3. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China;4. School of Computer and Software, Nanjing University of Information Science and Technology, Nangjing 210044, China;1. Beihang University, Beijing, China;2. Anhui University, Hefei 230601, China;3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK;1. School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, PR China;2. Dept. of Computer Science, University College London, London WC1E 6EA, UK;3. National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing, PR China;4. Department of Computer Science, School of Science at Loughborough University, UK;5. Department of Information Engineering and Computer Science, University of Trento, Italy;1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China;2. National Engineering Lab on Information Content Analysis Techniques, GT036001 Shanghai, China;3. Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China |
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| Abstract: | Seam carving is the most popular content-aware image retargeting technique. However, it may also be used to correct poor photo composition in photography competition or to remove object from image for malicious purpose. A blind detection approach is presented for seam carved image with low scaling ratio (LSR). It exploits spatial and spectral entropies (SSE) on multi-scale images (candidate image and its down-sampled versions). We observe that when a few seams are deleted from an original image, its SSE distribution is greatly changed. Forty-two features are designed to unveil the statistical properties of SSE in terms of centralized tendency, dispersion tendency and distribution tendency. They are combined with the local binary pattern (LBP)-based energy features to form ninety-six features. Finally, support vector machine (SVM) is exploited as classifier to determine whether an image is original or suffered from seam carving. Experimental results show that the proposed approach achieves superior detection accuracy over the state-of-the-art works, especially for resized image by seam carving with LSRs. Moreover, it is robust against JPEG compression and seam insertion. |
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| Keywords: | Image forensics Content-aware image retargeting Seam carving Low scaling ratios Spatial and frequency entropy Object removal |
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