Encrypted Image Retrieval System based on Features Analysis
Keywords:Content Based Image Retrieval, Secure Content Based Image Retrieval, Image Mining, Similar Image Retrieval, Bloom Filter.
Abstract – Content-based search provides an important tool for users to consume the ever-growing digital media repositories. However, since communication between digital products takes place in a public network, the necessity of security for digital images becomes vital. Hence, the design of secure content-based image retrieval system is becoming an increasingly demanding task as never before. This paper, presents a mechanism that addresses the secure CBIR as a novel improvement and application for the image retrieval. The proposed system consists of six phases briefly described as follows: first, feature extraction phase, which produces the low-level quantitative description of the image (color and texture) that allows the computation of similarity measures, the definition of the ordering of the images, and the indexing of the search processes. Second, indexing for search process phase, hash table and bloom filter were employed for classification. Third, feature encryption phase, where content protection is performed using a method developed by us (including Chaotic Logistic Map). Fourth, image encryption phase, as security mechanism for CBIR, we combine two research fields in computer science, CBIR and image cryptography, which grow up to meet the trends of security and speed in current computer sciences, chaos and stream cipher systems were applied as an image encryption system. Fifth, the retrieval phase, which provides a subset of images answering the query based on the similarity between images computed over the feature vector extracted from each image. Finally, Relevance feedback phase, a technique that attempts to capture the user’s needs through iterative feedback. Although the system proved its efficiency in search performance (with 88% of average precision), security strength, and computational complexity, it does not mean the optimal system is designed, since some weakness points still can be found that are suggested to be improved as a future work.
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