Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion

TL;DR

This paper introduces a novel image tampering localization method using ConvNeXt and multi-scale feature fusion, significantly improving accuracy and robustness over existing techniques.

Contribution

It presents a new ConvNeXt-based architecture with multi-scale feature fusion and data augmentation for enhanced tampering localization.

Findings

Outperforms state-of-the-art localization methods

Achieves higher accuracy and robustness

Effective in diverse tampering scenarios

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 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.