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Development of Digital Image Forgery Detection Method Utilizing LE(Local Effect) Operator based on L0 Norm

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2020, 16(2), pp.153-162
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : December 7, 2020
  • Accepted : December 21, 2020
  • Published : December 31, 2020

Chol Yong Soo 1

1신한대학교

Candidate

ABSTRACT

Digital image forgery detection is one of very important fields in the field of digital forensics. As the forged images change naturally through the advancement of technology, it has made it difficult to detect forged images. In this paper, we use passive forgery detection for copy paste forgery in digital images. In addition, it detects copy-paste forgery using the L0 Norm-based LE operator, and compares the detection accuracy with the forgery detection using the existing L2, l1 Norm-based LE operator. In comparison of detection rates, the proposed lower triangular(Ayalneh and Choi) window was more robust to BAG mismatch detection than the conventional window filter. In addition, in the case of using the lower triangular window, the performance of image forgery detection was measured increasingly higher as the L2, L1 and L0 Norm LE operator was performed.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.