Hough transform generated strong image hashing scheme for copy detection

Srivastava, Mayank and Siddiqui, Jamshed and Ali, Mohammad Athar (2018) Hough transform generated strong image hashing scheme for copy detection. Journal of Information and Communication Technology, 17 (4). pp. 653-678. ISSN 2180-3862

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The rapid development of image editing software has resulted in widespread unauthorized duplication of original images. This has given rise to the need to develop robust image hashing technique which can easily identify duplicate copies of the original images apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on discrete wavelet transform and Hough transform, which is robust to large number of image processing attacks including shifting and shearing. The input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is then applied to the pre-processed image to produce different wavelet coefficients from which different edges are detected by using a canny edge detector. Hough transform is finally applied to the edge-detected image to generate an image hash which is used for image identification. Different experiments were conducted to show that the proposed hashing technique has better robustness and discrimination performance as compared to the state-of-the-art techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique towards various image processing attacks. The proposed copy detection scheme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system.

Item Type: Article
Uncontrolled Keywords: Content-based copy detection, digital watermarking, discrete wavelet transform, hough transform, image forensics, image hashing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General) > Management information systems
Divisions: School of Computing
Depositing User: Athar Ali
Date Deposited: 11 Jun 2019 08:54
Last Modified: 11 Jun 2019 08:54
URI: http://bear.buckingham.ac.uk/id/eprint/371

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