Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique


TAHAOĞLU G., ULUTAŞ G., ÜSTÜBİOĞLU B., NABIYEV V.

MULTIMEDIA TOOLS AND APPLICATIONS, vol.80, no.15, pp.23419-23456, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 80 Issue: 15
  • Publication Date: 2021
  • Doi Number: 10.1007/s11042-020-10241-9
  • Journal Name: MULTIMEDIA TOOLS AND APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, FRANCIS, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.23419-23456
  • Keywords: L*a*b* color space, Copy move forgery, Dynamic localization
  • Karadeniz Technical University Affiliated: Yes

Abstract

The wide availability of easy-to-use image editors has made the authenticity of images questionable. Copy-move is one of the most applied forgery types. A new copy-move forgery detection and localization technique independent from the characteristics of the forged regions is proposed in this paper. SIFT keypoints are obtained from CLAHE applied sub-images extracted from the input image by using RGB and L*a*b* color-spaces. Keypoint matching is realized on the sub-images and duplicated regions are determined roughly to create roughly marked image R. RANSAC is also applied in this stage and generated homography matrix is used to construct transformed roughly marked image R-'. The method extracts DCT based features from R and R-' to localize exact borders of the tampered regions on the roughly determined areas by using a dynamic threshold. The proposed method has a new suggestion to determine the threshold dynamically. Tamper localization procedure also utilizes from morphological operations (chosen depending on the characteristic of the image) and Connected Component Labeling to determine exact forge boundaries. Results indicate that the proposed method has a better performance compared with state-of-the-art copy-move forgery detection methods on the GRIP dataset. Scaling attack performance of the method is especially better than similar works as shown in the results.