A Technique to Detect Copy-Move Forgery using Enhanced SURF
Payal Srivastava1, Manoj Kumar2, Vikas Deep3, Purushottam Sharma4
1Payal Srivastava, Department of Information Technology ASET, AMITY University, Noida (U.P), India.
2Manoj Kumar, Department of Computer Science ASET, AMITY University, Noida (U.P), India.
3Vikas Deep, Department of Information Technology ASET, AMITY University, Noida (U.P), India.
4Purushottam Sharma, Department of Information Technology ASET, AMITY University, Noida (U.P), India.
Manuscript received on 16 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 06 September 2019 | PP: 676-680 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11330886S19/19©BEIESP | DOI: 10.35940/ijeat.F1133.0886S19
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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: The field of digital imaging has advanced in recent years with increase of various digital gadgets and applications associated to it. With easily availability of image editing softwares which are either free of cost or budget friendly, image can be effortlessly tampered by the means of making forgery. This has led to increase in crime related to various image processing and computer vision applications. To combat with such forgeries digital forensic provide scientific techniques to identify whether image is original or forged. The proposed work implemented a image forgery check system based on SURF features. This is a pixel based technique where after preprocessing the images, relevant features are extracted and compared with a defined estimated threshold value. Based on the demonstrated results it is decided whether the image has been forged or not and if it is, then the area where tampering has been done is displayed as a forged part. The proposed algorithm is tested using open source CASIA image dataset. Also, the presented result shows that SURF feature based authentication provide forgery detection accuracy of 97%. The results are compared with other techniques in similar domain to prove the novelty of the work.
Keywords: Image Tampering, Forgery Detection, Advanced SURF, Copy-Move Forgery.
Scope of the Article: Advanced Manufacturing Technologies