With the continuous development of digital image processing technology, image tampering is no longer limited to a single method such as image splicing, rather the traces of malicious tampering are concealed in the post processing through the image editing software. This new development leads to poor results of traditional image forgery detection algorithms and the tampering localization methods based on deep learning. Aiming at the problem of low accuracy of existing image tampering algorithms, this study proposed an end-to-end image tampering location network based on multi-scale visual Transformer. The network combines a transformer and a convolutional encoder to extract the feature difference between the tampered area and the non-tampered area. Multi-scale visual Transformer models the spatial information of image block sequences of different sizes, so that the network can adapt to tampered areas of various shapes and sizes. Experimental results show that the F₁ and AUC scores of the proposed algorithm in the CASIA and NIST2016 test sets are 0.431、0.877、0.728 and 0.971, respectively. This shows that the performance of the new algorithm is significantly better than that of the existing mainstream algorithms. Moreover, the proposed algorithm is robust against JPEG compression attacks.