A new Copy-Move forgery detection method using LIOP

AYDIN Y.

Journal of Visual Communication and Image Representation, cilt.89, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 89
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.jvcir.2022.103661
  • Dergi Adı: Journal of Visual Communication and Image Representation
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Communication & Mass Media Index, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: Copy-move forgery, LIOP, YCbCr, Keypoint, Image processing
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier Inc.The most prevalent type of digital image falsification occurs when a portion of a image is copied and pasted onto another section of the same image. Falsification of the image made in this way is called copy-move forgery (CMF). This study presents a new and effective approach for copy-move forgery detection (CMFD) using the Local Intensity Order Pattern (LIOP) to overcome the restrictions of existing CMFD techniques. The input image is first converted to a YCbCr color space and then split into Y, Cb, and Cr color channels. The LIOP features are then extracted from each color channel and all the features are combined. The feature vectors are ordered lexicographically and related features are detected by comparing the LIOP features. Although the LIOP feature has rarely been used in CMFD prior to this study, the success rate of the proposed method is high. In addition, since the channels are not correlated to each other in the YCbCr color space, each color channel is considered as a gray image, and the success rate is increased by combining the features extracted from each of the color channels. The proposed approach was assessed using the CoMoFoD and GRIP datasets. Experimental findings demonstrated that the suggested method was successful and displayed robustness in post-processing attacks.