Effective image tampering localization with multi-scale ConvNeXt feature fusion

Abstract

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 encoder and multi-scale Feature Fusion (ConvNeXtFF). Stacked ConvNeXt blocks are utilized as an encoder to capture hierarchical multi-scale features, which are then fused in decoder for locating tampered pixels accurately. Combined loss function and effective data augmentation strategies are adopted to further improve the model performance. Extensive experimental results show that both localization accuracy and robustness of the ConvNeXtFF scheme outperform other state-of-the-art ones. The source code is available at https://github.com/multimediaFor/ConvNeXtFF.