Image inpainting is a common method of image tampering. Image inpainting methods based on deep learning can generate more complex structures and even new objects， making image inpainting forensics more challenging. Therefore， an end-to-end U-shaped Feature Pyramid Network （FPN） was proposed for image inpainting forensics. Firstly， multi-scale feature extraction was performed through the from-top-to-down VGG16 module， and then the from-bottom-to-up feature pyramid architecture was used to carry out up-sampling of the fused feature maps， and a U-shaped structure was formed by the overall process. Next， the global and local attention mechanisms were combined to highlight the inpainting traces. Finally， the fusion loss function was used to improve the prediction rate of the repaired area. Experimental results show that the proposed method achieves an average F1-score and Intersection over Union （IoU） value of 0.791 9 and 0.747 2 respectively on various deep inpainting datasets. Compared with the existing Localization of Diffusion-based Inpainting （LDI）， Patch-based Convolutional Neural Network （Patch-CNN） and High-Pass Fully Convolutional Network （HP-FCN） methods， the proposed method has better generalization ability， and also has stronger robustness to JPEG compression.