With the continuous development of image editing technologies， it is particularly significant to develop image forensics technologies for image content security. Most existing forensics methods concentrated on single image manipulation detection but with weak robustness and no considerations on tampering location. This paper presents a multi-manipulation image forgery detection method based on convolutional neural network. In this network， a convolution flow based on residual block is constructed to extract manipulation features. Then， a multi-scale feature fusion module is designed to achieve operational feature fusion at different scales. Finally， the fused manipulation features are fed into the multi-branch prediction module， predicting the type and location of each utilized manipulation as the multi-manipulation detection results. An image dataset produced by multiple typical image manipulations is built to train and test the proposed network. The experimental results show that the proposed scheme can recognize the type of tampered manipulations and locate the tampered area more accurately with fewer parameters， and has better robustness to common image post-processing operations， compared with the state-of-the-art object detection networks.