Abstract: Recently, image splicing forgery detection methods based on convolutional neural networks (CNNs) have been widely studied with continuous advancements. However, the performance of most existing models may not be satisfied caused by objects with various types and sizes. In this paper, we propose a new integrated multi-scale attention network to accommodate these problems. Specifically, we append two types of self-attention modules, namely, position attention model and channel attention model, between two convolution layers in feature extraction procedure. For position attention model, we emphasize the semantic interdependencies in spatial dimension by capturing the relationships between any two feature positions so that each pixel can perceive the information of the rest of the pixels. For channel attention model, we apply similar self-attention operations to capture the relationships between any two-channel maps in order that each pixel can perceive the information of other channel pixels. Meanwhile, by dividing the feature maps into multiple subregions, our attention modules can better preserve and highlight the details while capturing long-range semantic information dependencies, which not only concern the spliced forgeries of various sizes but also reduce the computational cost for feature maps with high resolutions. Experimental results show that the F₁ and IoU of the integrated multi-scale attention network algorithm on the CASIA test set are 62.3% and 61.2%, respectively, which are significantly improved compared to other existing algorithms.