Image Forgery Detection

Abstract

In the digital age, the proliferation of image editing tools has made it easier than ever to manipulate images, raising concerns about the authenticity and credibility of visual content. This project focuses on the development of an effective and efficient image forgery detection system to address the growing challenges associated with digital image tampering. The proposed system leverages advanced techniques in computer vision and machine learning to detect common forms of forgeries, such as copy-move, splicing, and removal. Using feature extraction methods such as SURF, SIFT, and deep learning models, the system identifies inconsistencies in texture, lighting, and metadata. By employing a robust dataset of authentic and forged images for training and testing, the system achieves high accuracy in distinguishing tampered images from genuine ones. This project aims to contribute to areas such as digital forensics, content verification, and social media monitoring, ensuring trustworthiness in digital media. The results demonstrate the system's potential for real-world applications, providing an automated and reliable tool for image integrity verification.