Composite attacks‐based copy‐move image forgery detection using AKAZE and FAST with automatic contrast thresholding

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.

Small insulator target detection based on multi‐feature fusion

“…The corner detected by FAST is defined as follows: there are enough pixels in the neighbourhood around the pixel in different areas from the point [18, 19]. Consider any pixel in the image and a region centred on it.…”

“…The ORB algorithm is used to quickly create feature vectors for key points in an image, which can be used to identify The corner detected by FAST is defined as follows: there are enough pixels in the neighbourhood around the pixel in different areas from the point [18,19]. Consider any pixel in the image and a region centred on it.…”

Image copy-move forgery detection and localization based on super-BPD segmentation and DCNN

“…To reduce the computational complexity of block-based CMFD methods, the keypoint-based methods are proposed, using features of key points to replace that of blocks. The main key features are scale invariant feature transform (SIFT) 12 , speed-up robust feature (SURF) 1 , Harris 13 , accelerated-KAZE (A-KAZE) 14 , oriented FAST and rotated BRIEF (ORB) 15 , fast retina keypoint (FREAK) 16 , etc. However, most keypoints extraction methods extract few key points in the smooth regions, resulting in some forgeries in the smooth regions being ignored easily.…”

Detection of Copy-Move Forgery Using Euclidean Distance and Texture Features

“…CFM detection boasts bright prospects in legal evidence, forensic investigation, defense, and many other fields. Dixit and Bag [23] discussed a CFM detection tool with the potential to identify sections of a picture that have been copied and paste. The CFM detection tool identifies forgeries in two steps.…”