Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion
This addresses the need for robust image forensic tools to detect tampering, but it appears incremental as it builds on existing ConvNeXt and feature fusion methods.
The paper tackles the problem of localizing tampered pixels in images by proposing a scheme using ConvNeXt networks and multi-scale feature fusion, achieving state-of-the-art localization performance as shown in extensive experiments.
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.