Multi-Task SE-Network for Image Splicing Localization

Image splicing can be easily used for illegal activities such as falsifying propaganda for political purposes and reporting false news, which may result in negative impacts on society. Hence, it is highly required to detect spliced images and localize the spliced regions. In this work, we propose a multi-task squeeze and excitation network (SE-Network) for splicing localization. The proposed network consists of two streams, namely label mask stream and edge-guided stream, both of which adopt convolutional encoder-decoder architecture. The information from the edge-guided stream is transmitted to the label mask stream for enhancing the discrimination of features between the spliced and host regions. This work has three main contributions. First, image edges, along with label masks and mask edges, are exploited to supply more comprehensive supervision for the localization of spliced regions. Second, the low-level feature maps extracted from shallow layers are fused with the high-level feature maps from deep layers to provide more reliable feature for splicing localization. Finally, several squeeze and excitation attention modules are incorporated into the network to recalibrate the fused features to enhance the feature expression. Extensive experiments show that the proposed multi-task SE-Network outperforms existing splicing localization methods evidently on two synthetic splicing datasets and four benchmark splicing datasets.