Visual Security Evaluation Based on SIFT Object Recognition

Abstract : The paper presents a metric for visual security evaluation of encrypted images based on object recognition using the Scale Invariant Feature Transform (SIFT). The metrics’ behavior is demonstrated using three different encryption methods and its performance is compared to that of the PSNR, SSIM and Local Feature Based Visual Security Metric (LFBVSM). Superior correspondance to human perception and better responsiveness to subtle changes in visual security are observed for the new metric.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.624-633, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_62〉
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Stefan Jenisch, Andreas Uhl. Visual Security Evaluation Based on SIFT Object Recognition. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.624-633, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_62〉. 〈hal-01391369〉

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