Abstract
Numerous methods have been developed to identify copy-move forgeries, which are among the most often used alteration strategies of digital photographs. The most widely used format of digital photographs is JPEG, which allows for high-rate compression without drastically altering the meaning of the picture. The objective of this work is to develop a system that can automatically identify the forgery type of the suspect image through in a single procedure, without requiring any kind of expert information. A preferable method is to run the same image through multiple algorithms, which saves time and prevents the needless evaluation of multiple detection results, from which it may be difficult to determine the correct output due to the presence of multiple confounding factors. Additionally, it has been shown that the established method is very effective in detecting expert forgeries when the duplicated region is picked in a non-rigid fashion, which is almost hard for the human eye to perform.
Publisher
Inventive Research Organization
Subject
General Agricultural and Biological Sciences
Reference27 articles.
1. [1] Soad Samir, Eid Emary, Khaled Elsayed, Hoda Onsi, Copy-Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction, Journal of Computer and Communications, Vol.7 No.9, September 2019, DOI: 10.4236/jcc.2019.79001.
2. [2] A. Kashyap, R. S. Parmar, M. Agrawal, and H. Gupta, "An Evaluation of Digital Image Forgery Detection Approaches," arXiv preprint arXiv:1703.09968, 2017.
3. [3] N. K. Gill, R. Garg, and E. A. Doegar, "A review paper on digital image forgery detection techniques," in Computing, Communication and Networking Technologies (ICCCNT), 2017 8th International Conference on, 2017, pp. 1-7.
4. [4] T. M. Mohammed, J. Bunk, L. Nataraj, J. H. Bappy, A. Flenner, B. Manjunath, et al., "Boosting Image Forgery Detection using Resampling Detection and Copy-move analysis," arXiv preprint arXiv:1802.03154, 2018.
5. [5] O. Mayer and M. C. Stamm, "Accurate and Efficient Image Forgery Detection Using Lateral Chromatic Aberration," IEEE Transactions on Information Forensics and Security, 2018.