DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention
This addresses the risk of AI-generated forged images, which is an incremental advance in domain-specific detection and localization.
The paper tackles the problem of detecting and localizing forged images generated by AI, constructing a new dataset and proposing a hierarchical progressive network with dual-attention and multi-branch interaction to capture forged artifacts at different scales, achieving significant performance improvements over state-of-the-art methods.
The increasing difficulty in accurately detecting forged images generated by AIGC(Artificial Intelligence Generative Content) poses many risks, necessitating the development of effective methods to identify and further locate forged areas. In this paper, to facilitate research efforts, we construct a DA-HFNet forged image dataset guided by text or image-assisted GAN and Diffusion model. Our goal is to utilize a hierarchical progressive network to capture forged artifacts at different scales for detection and localization. Specifically, it relies on a dual-attention mechanism to adaptively fuse multi-modal image features in depth, followed by a multi-branch interaction network to thoroughly interact image features at different scales and improve detector performance by leveraging dependencies between layers. Additionally, we extract more sensitive noise fingerprints to obtain more prominent forged artifact features in the forged areas. Extensive experiments validate the effectiveness of our approach, demonstrating significant performance improvements compared to state-of-the-art methods for forged image detection and localization.The code and dataset will be released in the future.