Most of existing methods exhibit poor performance in detecting forged images due to the small size of tampered areas and the limited pixel difference between untampered and tampered regions. To alleviate the above problem, a double-branch tampered image detection based on multi-scale features is proposed. Firstly, we introduce a fusion module based on attention mechanism in the first branch to enhance the network's sensitivity towards tampered regions. Secondly, we construct a second branch specifically designed for detection, aiming to identify subtle differences between tampered and untampered areas by utilizing rich edge information from shallow features as guidance. Compared to the existing methods on the public benchmark datasets CASIA1.0, Columbia and NIST16, the values of F-score reached 0.766, 0.900 and 0.930 on those datasets, respectively. The experimental results show that our method could significantly improve the accuracy on detecting the tampered area.