Robust Copy-Paste Detection Algorithm using SIFT for Digital Image Forensics
Amit Kumar Nag1, Bhumiphat Gilitwala2
1Monika, Assistant Professor, Manav Rachna International Institute of Research and Studies, Faridabad, NCR, India.
2Dipali Bansal, Associate Dean – Academics with Manav Rachna International Institute of Research and Studies, Faridabad, NCR, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3616-3627 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7841118419/2019©BEIESP | DOI: 10.35940/ijrte.D7841.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Forensics of images verifies the authenticity of digital images. Because of the easier availability of software, manipulation of images has become quicker and easier. Image composition, copy-paste, multiple cloning, splicing, etc have become a common practice. The paper proposes a robust algorithm for the detection of duplicity using Scale Invariant Feature Transform (SIFT) approach. Copied location of an image is occasionally pasted in another place of the identical image or in another image, which creates difficulties in detecting the copy-paste region and identifying the located region as well as creates inefficiency in the accuracy of forgeries. We developed an approach that shows improved techniques through runtime optimizations and compared various parameters with existing methodologies in order to obtain highly correlated image areas for detecting the manipulated regions. The proposed approach can detect copy-paste forgeries effectively with high accuracy, reliability, and inconsistencies regardless of the test scenario.
Keywords: CMTD, Digital Image Forensic, Image Forgery Detection, Image Authentication, SIFT.
Scope of the Article: Plant Cyber Security.