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.

使用双重关注机制及多分支交互网络，在不同尺度上捕捉伪造图像的伪造特征，有效提高伪造图像检测和定位的性能。