Image Forgery Detection using CNN
Meet Patel, Kartikay Rane, Niyati Jain, Praneel Mhatre, Shree Jaswal · 2023
With the increasing use of digital images in various applications, the problem of image forgery has become more prevalent than ever. In this paper, we propose a novel image forgery detection system based on Convolutional Neural Networks (CNNs) that can detect various types of image manipulations, including copy-move, splicing, and retouching. Our proposed system integrates Error Level Analysis (ELA) with deep learning techniques to provide a more accurate and reliable solution to the problem of image forgery detection. We evaluated the proposed system on a dataset of real-world images and achieved a high detection accuracy of 93%. Our system outperformed existing methods for image forgery detection and demonstrated its potential for various applications, including forensics, security, and digital image analysis. Overall, the proposed CNN-based image forgery detection system offers a robust and effective solution to the growing problem of image manipulation and forgery in today's visual media landscape.