Enhancing Image Forgery Detection with Convolutional Neural Networks and Error Level Analysis

Abstract

The growing prevalence of image manipulation poses critical challenges to the reliability of visual content across fields like journalism, law enforcement, and digital forensics. Traditional methods often struggle to detect complex forgeries, especially in cases like splicing or copy-move operations, and lack the efficiency to process large datasets in real time. To address these issues, this research introduces a novel image forgery detection framework combining Convolutional Neural Networks (CNNs) and Error Level Analysis (ELA). By leveraging CNNs for pixel-level anomaly detection and ELA for identifying compression inconsistencies, the system effectively detects various manipulations with improved precision and scalability. Extensive testing on a diverse dataset revealed a high accuracy rate exceeding 94%, underscoring the system’s potential for real-time applications. This comprehensive approach represents a major leap forward in image authentication, offering a reliable solution to uphold the integrity of visual content in today’s digitally manipulated world.