نظام استرجاع الصور المشفرة بالاعتماد على تحليل الخصائص == Retrieval System of Encrypted Images based on Features Analysis
Author name:
فادية فؤاد حنتوش
Supervisor name:
ميثاق طالب كاطع
General topic:
Computer Science
Specific topic:
Computer Science
Degree:
Master
University:
Mustansiriyah University - College Of Science - Department Of Computer
Language:
English
University location:
Baghdad
First pages:
28T844 - p.pdf
Abstract:
To search in image collections based on visual content is potentially a very powerful technique. 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 (SCBIR) system is becoming an increasingly demanding task as never before.This thesis, presents a mechanism that addresses the SCBIR 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 phase, Hash table and Bloom filter were employed for classification. Third, feature encryption phase, where content protection is performed using Chaotic Logistic Map (CLM) and logical operations. Fourth, image encryption phase, as a security mechanism for CBIR, two research fields in computer science was combined, CBIR and image cryptography, which grow up to meet the trends of security and speed in current computer sciences, CLM and Rivest cipher 4 algorithms were applied. Fifth, 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 security strength, computational complexity, and search performance with 88% of average precision, it does not mean the optimal system was designed, since some weakness points still can be found that are suggested to be improved as a future work.
