Title: Deep learning-based image forgery detection system

Authors: Helina Rajini Suresh; M. Shanmuganathan; T. Senthilkumar; B.S. Vidhyasagar

Addresses: Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu 600062, India ' Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India ' Department of English, S.A. Engineering College (Autonomous), Chennai, Tamilnadu, India ' Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India

Abstract: Despite the fact that there are more complex ways of forgery being developed all the time, image forgery detection continues to play an essential part in the field of digital forensics. The problem of counterfeit photographs is a worldwide problem today that is mostly distributed via social networking sites. The ability to identify phoney pictures eliminates the possibility that fraudulent photographs may be used to trick or damage other people. Within the scope of this research, we investigate the deep learning technique to image forgery detection. The proposed model implemented by python language uses input images in batches and a convolutional neural network (CNN) using ResNet50v2 architecture and YOLO weights. We analysed the CASIA v1 and CASIA v2 benchmark datasets. For the purposes of training, we used 80% of the data, and the remaining 20% was used for testing. 85% accuracy obtained for the dataset.

Keywords: machine learning; deep learning; image forgery; ResNet50; YOLO; CNN.

DOI: 10.1504/IJESDF.2024.137036

International Journal of Electronic Security and Digital Forensics, 2024 Vol.16 No.2, pp.160 - 172

Received: 27 Sep 2022
Accepted: 24 Nov 2022
Published online: 01 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article