Image manipulation detection by multiple tampering traces and edge artifact enhancement

Image manipulation detection has attracted considerable attention owing to the increasing security risks posed by fake images. Previous studies have proven that tampering traces hidden in images are essential for detecting manipulated regions. However, existing methods have limitations in generalization and the ability to tackle post-processing methods. This paper presents a novel Network to learn and Enhance Multiple tampering Traces (EMT-Net), including noise distribution and visual artifacts. For better generalization, EMT-Net extracts global and local noise features from noise maps using transformers and captures local visual artifacts from original RGB images using convolutional neural networks. Moreover, we enhance fused tampering traces using the proposed edge artifacts enhancement modules and edge supervision strategy to discover subtle edge artifacts hidden in images. Thus, EMT-Net can prevent the risks of losing slight visual clues against well-designed post-processing methods. Experimental results indicate that the proposed method can detect manipulated regions and outperform state-of-the-art approaches under comprehensive quantitative metrics and visual qualities. In addition, EMT-Net shows robustness when various post-processing methods further manipulate images.