Adaptive Keypoint Selection for Detection of Tampering in Images and Videos
Sonal Patil1, K. N. Jariwala2
1Sonal Patil, Department of Computer Science Engineering, S.V.N.I.T., Surat, India.
2Dr. Krupa N. Jariwala, Assistant Professor Department of Computer Engineering, S.V.N.I.T., Surat, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 235-239 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10660688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Tampering with images and videos for duplicating content and copyright infringement has become a very common problem for original content producers. The main issue with duplication and forgery is that, due to the advancement of forging techniques, it is being increasingly difficult in terms of both computational power and algorithmic complexity to detect and trace the forgeries with good level of accuracy. In this paper, we propose an adaptive keypoint based approach to detect the presence of forgery in images. Our approach is independent of the input dataset, and provides good level of accuracy for forgery detection. The system is tested on REWIND dataset, and an accuracy of more than 85% was observed. Our approach can be further extended to incorporate machine learning in order to improve the accuracy.
Keywords: Tampering, forgery, keypoint, REWIND, complexity.
Scope of the Article: Image Analysis and Processing