MFI-Net: Multi-Feature Fusion Identification Networks for Artificial Intelligence Manipulation

Tampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, etc. With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, e.g., localization, segmentation, and alpha-matting, etc. Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.