HFRRU-Net: Hybrid Features Ringed Residual U-Net for Image Splicing Forgery Detection

With the rapid development of computers and the internet, digital image forgery detection has become one of the important research hot topics in the field of computer vision. In this article, we propose a dual stream network to detect image tampering and locate forged areas. Our network includes the ringed residual U-Net, RGB stream and DCT stream, which is named as a hybrid feature ringed residual U-Net (HFRRU-Net) because of combining frequency domain features with RGB features. Compared to the existing works, HFRRU-Net is an end-to-end training approach without image preprocessing or post-processing, and this network may speed up the training and prediction process. In the experments, the HFRRU-Net is evaluated on five public image forgery datasets, CASIA v1.0, Columbia, In-The- Wild, Nist2016, and Realistic. The results indicate that the F1 scores of the HFRRU-Net outperforms other methods in four datasets. And visual comparisons further demonstrate the proposed method.