TAILIEUCHUNG - Hough transform generated strong image hashing scheme for copy detection

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. | Journal of ICT, 17, No. 4 (October) 2018, pp: 653–678 How to cite this article: Srivastava, M. Siddiqui, J., & Ali, M. A. (2018). Hough transform generated strong image hashing scheme for copy detection. Journal of Information and Communication Technology, 17(4), 653-678. HOUGH TRANSFORM GENERATED STRONG IMAGE HASHING SCHEME FOR COPY DETECTION Mayank Srivastava, 2Jamshed Siddiqui & 3Mohammad Athar Ali 1 Institute of Engineering and Technology, Ganeshi Lal Agrawal University, India 2 Department of Computer Science, Aligarh Muslim University, India 3 Department of Applied Computing, University of Buckingham, United Kingdom 1 ; jamshed_faiza@; ABSTRACT 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-theart techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique Received: 7 April 2018 Accepted: 30 August .

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