HRDA-Net: image multiple manipulation detection and location algorithm in real scene - Ye ZHU; Yilin YU; Yingchun GUO (Tongxin xuebao)
Aiming at the problems that the fake image just contains one tampered operation in mainstream manipulation datasets and the artifact is a common problem in manipulation location.The multiple manipulation dataset (MM Dataset) was constructed for real scene, which contained both splicing and removal in each images.Based on this, an end-to-end high-resolution representation dilation attention network (HRDA-Net) was proposed for multiple manipulation detection and localization, which fused the RGB and SRM features through the top-down dilation convolutional attention (TDDCA).Finally, the mixed dilated convolution (MDC) would respectively extract the features of splicing and removal, which could realize multiple manipulation location and confidence prediction.The cosine similarity loss was proposed as auxiliary loss to improve the efficiency of network.Experimental results on MM Dataset indicate that the performance and robustness of HRDA-Net is better than semantic segmentation methods.Furthermore, the scores of F1 and AUC are greater than state-of-the-art manipulation location methods in CASIA and NIST datasets.