SURF Based Copy Move Forgery Detection Using kNN Mapping

Kelvin Harrison Paul, K. R. Akshatha, A. K. Karunakar, Sharan Seshadri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Digital images can be edited with the help of photo editing tools to improve or enhance the image quality. On the other hand, digital images can also be subject to manipulations which can alter the visual information being conveyed by the image. The forged images can also be used to spread false information through various media platforms and in some cases may be surreptitiously used as false evidence in a court of law. Therefore, it is crucial to test the authenticity of such images and ensure that it does not spread falsified information. One of the most common types of forgery being used today is copy-move forgery in which one part of the image is copied and placed over another part of the same image in order to either conceal certain details or multiply certain features seen in the original image. This work introduces a method of detecting copy-move forgery in digital images using speeded-up robust features (SURF) to extract keypoints from the image and then uses k-nearest neighbor (kNN) training and mapping to yield accurate matches. The SURF algorithm is capable of performing equally or even exceed the more widely accepted SIFT-based counterparts in terms of ensuring distinctive features, reproducibility, and robustness. As a result, this technique ensures a robust detection of copy move forgery while ensuring lower computational costs compared to the SIFT-based techniques used for the same purpose.

Original languageEnglish
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
EditorsKohei Arai, Supriya Kapoor
PublisherSpringer Verlag
Pages234-245
Number of pages12
ISBN (Print)9783030177973
DOIs
Publication statusPublished - 01-01-2020
EventComputer Vision Conference, CVC 2019 - Las Vegas, United States
Duration: 25-04-201926-04-2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume944
ISSN (Print)2194-5357

Conference

ConferenceComputer Vision Conference, CVC 2019
CountryUnited States
CityLas Vegas
Period25-04-1926-04-19

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Image quality
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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Paul, K. H., Akshatha, K. R., Karunakar, A. K., & Seshadri, S. (2020). SURF Based Copy Move Forgery Detection Using kNN Mapping. In K. Arai, & S. Kapoor (Eds.), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC (pp. 234-245). (Advances in Intelligent Systems and Computing; Vol. 944). Springer Verlag. https://doi.org/10.1007/978-3-030-17798-0_20
Paul, Kelvin Harrison ; Akshatha, K. R. ; Karunakar, A. K. ; Seshadri, Sharan. / SURF Based Copy Move Forgery Detection Using kNN Mapping. Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. editor / Kohei Arai ; Supriya Kapoor. Springer Verlag, 2020. pp. 234-245 (Advances in Intelligent Systems and Computing).
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Paul, KH, Akshatha, KR, Karunakar, AK & Seshadri, S 2020, SURF Based Copy Move Forgery Detection Using kNN Mapping. in K Arai & S Kapoor (eds), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Advances in Intelligent Systems and Computing, vol. 944, Springer Verlag, pp. 234-245, Computer Vision Conference, CVC 2019, Las Vegas, United States, 25-04-19. https://doi.org/10.1007/978-3-030-17798-0_20

SURF Based Copy Move Forgery Detection Using kNN Mapping. / Paul, Kelvin Harrison; Akshatha, K. R.; Karunakar, A. K.; Seshadri, Sharan.

Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. ed. / Kohei Arai; Supriya Kapoor. Springer Verlag, 2020. p. 234-245 (Advances in Intelligent Systems and Computing; Vol. 944).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Paul KH, Akshatha KR, Karunakar AK, Seshadri S. SURF Based Copy Move Forgery Detection Using kNN Mapping. In Arai K, Kapoor S, editors, Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Springer Verlag. 2020. p. 234-245. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-17798-0_20