MSA-Net: Multi-scale attention network for image splicing localization

In this paper, we introduce the novel Multi-Scale Attention Network (MSA-Net) to address the challenge of locating diverse types and sizes of splicing forgery objects. Previous methods neglect crucial characteristics of global dependencies and size, resulting in imprecise localization on background tampering and small target tampering. To overcome this, we integrate a multi-scale self-attention mechanism to capture global dependencies and fully understand the relationships between spliced objects and untampered areas. Our approach involves inserting multi-scale attention modules that combine the position attention and channel attention modules between convolution layers for feature extraction. The position attention module emphasizes spatial interdependencies, capturing relationships between feature positions. Similarly, the channel attention module captures relationships between channel features. This allows for the preservation of intrinsic details while capturing long-range semantic dependencies, which is beneficial to the detection of splicing forgery objects. Meanwhile, by dividing the feature maps into multiple sub-regions or sub-channels, our attention modules can better preserve the details while capturing long-range semantic information dependencies. Experimental results show that the proposed MSA-Net outperforms several state-of-the-art algorithms with an F1-score of 60.5% and an IOU value of 58.8% on the CASIA dataset.