Accurate edge region information is required to correctly locate the tampered region of an image, but post-tampering processing methods such as blurring and smoothing make the tampered edge information lost. A multi-scale supervised image tampering detection method based on edge feature enhancement is proposed to make full use of the boundary features of tampered images and to improve the accuracy of image tampering region localization. A feature enhancement module is used to fuse tampered region edge features and tampered region features, and a dual attention decoding module is used to enhance the fused feature information and supervise the model training process at multiple scales. The experimental results show that the method outperforms the mainstream tamper location methods in terms of AUC and F1, and has better detection performance for different types of tampering.