Title: S-transform-based efficient copy-move forgery detection technique in digital images
Authors: Rajeev Rajkumar; Sudipta Roy; Khumanthem Manglem Singh
Addresses: Department of Computer Science and Engineering, Assam University, Silchar – 788011, Assam, India ' Department of Computer Science and Engineering, Assam University, Silchar – 788011, Assam, India ' Department of Computer Science and Engineering, National Institute of Technology, Imphal – 795001, Manipur, India
Abstract: Copy-move forgery (CMF), which copies a part of a picture and pastes it into another location, is one of the common strategies for digital image tampering. Due to the arrival of high-performance hardware and the compact use of image processing software, empowers creating image forgeries easy that are undetectable by the naked eye. For CMF detection, we suggest an efficient and vigorous method that could take care of numerous geometric ameliorations including rotation, scaling, and blurring. In the projected CMF detection system, we use Stockwell transform (S-transform) which hybrids the advantages of both scale invariant feature transform (SIFT) and wavelet transform (WT) to extract the key points and their descriptors from the overlapped image blocks. Furthermore, Euclidean distance (ED) between the overlapped blocks are measured to detect the similarities and to identify the tampered or forged region in the image. Besides, a novel fuzzy min-max neural network-based decision tree (FMMNN-DT) classifier is used to recognise the duplicated regions in the forgery image. The proposed system is tested and validated using MICC-F220 dataset and we present comparison among the proposed outcomes with some existing ones which ensure the significance of the proposed.
Keywords: copy-move forgery; CMF; Stockwell transform; S-transform; feature extraction; fuzzy min-max classifier; decision tree classifier.
International Journal of Intelligent Enterprise, 2020 Vol.7 No.1/2/3, pp.107 - 121
Received: 28 Jun 2018
Accepted: 25 Sep 2018
Published online: 27 Jan 2020 *