Aiming at the problem that the existing image splicing detection network model has insufficient attention to edge information and is not good enough for pixel-level accurate localization effects,a DeepLabV3+ image splicing tampering forensic method incorporating a residual attention mechanism is proposed.The methods use an encoding-decoding structure to achieve pixel level image splicing tampering localization.In the coding stage,the efficient attention module is integrated into the residual module of ResNet101.The residual module is stacked to reduce the proportion of unimportant features and highlight the splicing tampering traces.Then,the spatial pyramid pooling module with hole convolution is used for multi-scale feature extraction.The obtained feature maps are stitched and then modelled by spatial and channel attention mechanisms for semantic information.In the decoding stage,the localization accuracy of the image splicing forgery region is improved by fusing multi-scale shallow and deep image features.The experimental results show that the localization accuracy of splicing tampering on CASIA 1.0,COLUMBIA and CARVALHO datasets reaches 0.761,0.742 and 0.745,respectively.The proposed method has better image splicing forgery region localization performance than some existing methods,and the network also has better robustness to JPEG compression.