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.

翻译：随着AIGC（人工智能生成内容）生成的伪造图像准确检测难度日益增加，其带来的诸多风险亟需开发有效方法以识别并进一步定位伪造区域。本文为促进相关研究，构建了一个由文本或图像辅助的GAN与扩散模型引导的DA-HFNet伪造图像数据集。我们的目标是利用分层渐进式网络捕获不同尺度的伪造伪影以实现检测与定位。具体而言，该方法依托双重注意力机制深度自适应融合多模态图像特征，继而通过多分支交互网络充分交互不同尺度的图像特征，并利用层间依赖关系提升检测器性能。此外，我们提取了更敏感的噪声指纹，以在伪造区域中获得更显著的伪造伪影特征。大量实验验证了我们方法的有效性，相较于伪造图像检测与定位领域的最先进方法，本方法展现出显著的性能提升。代码与数据集将于未来公开。