A Unified Approach for Copy-Move Forgery Detection Using CNN and SIFT Localization

In the digital age, images and videos serve as crucial information-sharing tools. Advanced editing techniques have facilitated widespread digital image manipulation. Copy-move forgery, a prevalent method, involves copying image segments to conceal or duplicate content. This study introduces a unified strategy that combines Convolutional Neural Networks (CNN) and Scale-Invariant Feature Transform (SIFT) localization. This integrated approach offers a robust solution for detecting copy-move forgery, effectively countering contemporary image tampering techniques. By merging CNN's feature learning and SIFT's distinctive feature identification, this method shows promise for enhancing image forensics and authenticity verification.