  With the widespread use of powerful image editing tools, image tampering
becomes easy and realistic. Existing image forensic methods still face
challenges of low generalization performance and robustness. In this letter, we
propose an effective image tampering localization scheme based on ConvNeXt
network and multi-scale feature fusion. Stacked ConvNeXt blocks are used as an
encoder to capture hierarchical multi-scale features, which are then fused in
decoder for locating tampered pixels accurately. Combined loss and effective
data augmentation are adopted to further improve the model performance.
Extensive experimental results show that localization performance of our
proposed scheme outperforms other state-of-the-art ones. The source code will
be available at https://github.com/ZhuHC98/ITL-SSN.
