Convolutional Neural Network-Based Techniques and Error Level Analysis for Image Tamper Detection

The rapid evolution of digital image editing tools has made detecting tampered images increasingly crucial, especially in law enforcement, media integrity, and scientific publications. This study introduces a robust method leveraging Convolutional Neural Networks (CNNs) combined with Error Level Analysis (ELA) to effectively detect and localize tampered areas in digital images. CNNs, known for their prowess in image recognition tasks, are trained on a diverse dataset of tampered and untampered images, enabling them to learn and detect subtle and complex tampering patterns. Following the CNN analysis, ELA is employed as a secondary verification step, scrutinizing the areas flagged by the CNN for inconsistencies in compression levels indicative of tampering. This integration enhances detection accuracy and reduces false positives. Experimental results demonstrate that the proposed method significantly outperforms existing methods, with high accuracy, precision, and recall in detecting various tampering artifacts. The findings confirm the system’s enhanced reliability and accuracy, making it a valuable tool for maintaining the integrity of digital images. This study not only fortifies the technological arsenal against image forgery but also sets a benchmark for future research in digital image forensics, offering a more reliable and accurate detection tool for practical applications.