MFDF-IML: Multi-Feature Dynamic Fusion for Image Manipulation Localization

With the rapid advancement of image tampering techniques, the authenticity of multimedia content is increasingly challenged, necessitating the development of robust Image Manipulation Localization (IML) technologies. This paper introduces a novel approach, Multi-Feature Dynamic Fusion for Image Manipulation Localization (MFDF-IML), which ad-dresses the limitations of existing methods by integrating Error Level Analysis (ELA) as a new forgery feature. ELA enhances feature diversity and robustness by capturing subtle variations through analyzing discrepancies at different compression levels. Additionally, MFDF-IML employs a dynamic gating mechanism to adaptively fuse multiple features, including SRM, Bayar, Noiseprint++, and ELA, adjusting their weights according to various forgery scenarios. This method also integrates features from Convolutional Neural Networks (CNN) and Vision Transformers (ViT), leveraging CNN's local feature extraction and ViT's global dependency modeling to significantly improve forgery localization precision. Extensive experiments demonstrate that MFDF-IML outperforms existing methods across diverse forgery scenarios, highlighting its potential in image forensics.