Revisiting copy-move forgery detection by considering realistic image with similar but genuine objects
This addresses a specific problem in digital image forensics for detecting forgeries in realistic scenes, representing an incremental improvement over prior methods.
The paper tackles the challenge of copy-move forgery detection in images containing similar but genuine objects, proposing a method that uses Scaled Harris Feature Descriptors to achieve high robustness against geometric transformations and post-processing, with experimental results showing it compares favorably to existing methods.
Many images, of natural or man-made scenes often contain Similar but Genuine Objects (SGO). This poses a challenge to existing Copy-Move Forgery Detection (CMFD) methods which match the key points / blocks, solely based on the pair similarity in the scene. To address such issue, we propose a novel CMFD method using Scaled Harris Feature Descriptors (SHFD) that preform consistently well on forged images with SGO. It involves the following main steps: (i) Pyramid scale space and orientation assignment are used to keep scaling and rotation invariance; (ii) Combined features are applied for precise texture description; (iii) Similar features of two points are matched and RANSAC is used to remove the false matches. The experimental results indicate that the proposed algorithm is effective in detecting SGO and copy-move forgery, which compares favorably to existing methods. Our method exhibits high robustness even when an image is operated by geometric transformation and post-processing