Abstract
Abstract: Image tampering has become a leading issue in the digital age, which has given rise to serious implications in various fields such as journalism, forensics and photography. Detecting manipulated images with high accuracy is important to ensure the authenticity and credibility of visual content. In this research paper, we propose a robust and effective approach for image tampering detection utilizing a concatenated ResNet and XceptionNet model with Error Level Analysis which has achieved an accuracy of 98.58%.
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