Composite attacks‐based copy‐move image forgery detection using AKAZE and FAST with automatic contrast thresholding. Issue 17 (3rd March 2021)

• Record Type: Journal Article
• Authors: Dixit, Anuja; Bag, Soumen
• Is Part Of: IET image processing. Volume 14:Issue 17(2020)
• Page Start: 4528
• Page End: 4542
• Publication Date: 2021-03-03
• Subjects: pattern clustering -- image matching -- feature extraction -- image forensics -- search problems; image tampering technique -- forgery technique -- manipulated image -- image content -- novel keypoint‐based technique -- forged images -- nonmaximal value suppression -- automatic contrast thresholding -- FAST keypoint extraction -- extracted keypoints -- correlation‐based nearest neighbour search technique -- keypoint descriptors -- matched keypoint clusters -- keypoint outliers -- image‐level -- pixel‐level copy‐move -- composite attacks‐based copy‐move image forgery; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.1118
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Copy‐move image forgery is one of the most popular image tampering technique which can be performed for vicious purposes. In this forgery technique, selected region is copied and pasted at different locations on the same image to produce a manipulated image. Such forgery is denigratory as it can alter the image content by hiding or appending visual information. In this study, the authors propose a novel keypoint‐based technique to detect forged images sustaining composite attacks consisting of various combinations of geometrical and post‐processing operations. In the authors' method, AKAZE and FAST techniques are used to extract keypoints from the image. Non‐maximal value suppression with automatic contrast thresholding is performed during FAST keypoint extraction. SIFT and DAISY descriptors are computed corresponding to extracted keypoints. PCA is applied over SIFT and DAISY descriptors to discard lower components which are sensitive to distortions occurred in images. They apply a correlation‐based nearest neighbour search technique to detect similarity among keypoint descriptors. HDBSCAN algorithm is applied to obtain matched keypoint clusters. Further, RANSAC algorithm is utilised for removal of keypoint outliers. In comparison to state‐of‐the‐art techniques, their approach achieve high F‐measure (%) and low FPR (%) for image‐level as well as pixel‐level copy‐move forgery detection.