Fusing Multi-scale Attention and Transformer for Detection and Localization of Image Splicing Forgery

Image forgery detection has attracted widespread attention due to the enormous spread of forged images on the internet and social media. Many existing methods lack global contextual information and ignore the interaction between multi-level representations, which are not conducive to localizing the multi-scale tampered regions. To overcome these limitations, we present a novel method to detect splicing forgery of images by utilizing cross-scale interaction attention and multi-level global information. Firstly, we design a cross-scale interactive attention (CSIA) module for aggregating multi-level convolutional features, focusing selectively on task-relevant information. It allows representations learned at different levels to communicate effectively with each other. Secondly, by introducing the transformer layers with dynamic position embedding, the proposed method can capture the multi-level global contextual correlations of the image in a more flexible way. Extensive experiments have shown that our proposed method outperforms state-of-the-art methods. It can effectively detect, and segment multi-scale tampered regions by aggregating multi-scale local and global feature relevance.