Image manipulation detection by multiple tampering traces and edge artifact enhancement

作者:

Highlights:

• highlights

• Present an end-to-end EMT-Net for image manipulation detection. By fusing and enhancing tampering traces, it precisely predicts pixel-level results against multiple content-changing manipulation techniques;

• To detect heterologous and homogenous manipulation, transformer-based noise and CNN-based RGB encoding branches are developed. The two branches explore and fuse multiple tampering traces (i.e., global and local noise, and visual artifact features) for better generalization;

• The proposed Edge decoding branch (EDB) reinforces tampering traces at different scales to distinguish boundary artifacts and natural object edges. Edge artifact enhancement (EAE) modules and edge supervision strategy in EDB extract subtle edge artifacts of manipulated regions despite applying post-processing methods;

• Experiments on six popular benchmarks indicate EMT-Net outperforming state-of-the-art approaches. EMT-Net is robust to images manipulated with various post-processing methods.

摘要

highlights•Present an end-to-end EMT-Net for image manipulation detection. By fusing and enhancing tampering traces, it precisely predicts pixel-level results against multiple content-changing manipulation techniques;•To detect heterologous and homogenous manipulation, transformer-based noise and CNN-based RGB encoding branches are developed. The two branches explore and fuse multiple tampering traces (i.e., global and local noise, and visual artifact features) for better generalization;•The proposed Edge decoding branch (EDB) reinforces tampering traces at different scales to distinguish boundary artifacts and natural object edges. Edge artifact enhancement (EAE) modules and edge supervision strategy in EDB extract subtle edge artifacts of manipulated regions despite applying post-processing methods;•Experiments on six popular benchmarks indicate EMT-Net outperforming state-of-the-art approaches. EMT-Net is robust to images manipulated with various post-processing methods.

论文关键词:Image manipulation detection,Transformer,Edge artifact enhancement,Edge supervision

论文评审过程:Received 18 May 2022, Revised 29 August 2022, Accepted 4 September 2022, Available online 6 September 2022, Version of Record 21 September 2022.

论文官网地址:https://doi.org/10.1016/j.patcog.2022.109026