Abstract
Although image processing and forensic computing are different fields, they have been involved in the same computer science research areas such as image forgery detection, in recent years. Image forgery detection is a new branch of image processing due to increased image manipulation tools. Thus, we proposed a new block-based image forgery detection method within this framework. In this research, we applied the latest and easiest application feature extraction method used in a new iris recognition system, called rotation invariant neighborhood-based binary pattern, on the block-based image forgery detection system. To the best of our knowledge, this is the first work that applies to block-based copy-move forgery detection systems. The proposed method has been evaluated for different block sizes on a well-known image database (CoMoFod) in the literature. Experimental studies showed that our method forgery detection accuracy rate incentive results are higher than the state-of-the-art block-based forgery detection methods.
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Gurunlu, B., Ozturk, S. (2022). Efficient Approach for Block-Based Copy-Move Forgery Detection. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_16
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DOI: https://doi.org/10.1007/978-981-16-4016-2_16
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