The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative cross appearance-edge learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an appearance-edge cross-attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at \url{https://github.com/Jenine-321/GenFace

翻译：逼真图像生成器的快速发展已达到一个关键节点，真实图像与篡改图像之间的差异越来越难以区分。因此，建立基准并推进检测数字篡改的技术成为一个紧迫问题。尽管已有多个公开的人脸伪造数据集，但其中的伪造人脸大多使用基于GAN的合成技术生成，并未涉及扩散模型等最新技术。扩散模型生成的图像多样性和质量已显著提升，因此需要采用更具挑战性的人脸伪造数据集来评估最先进的伪造检测方法。本文提出一个大规模、多样化、细粒度的高保真数据集——GenFace，以促进深度伪造检测技术的发展。该数据集包含大量由先进生成器（如基于扩散的模型）生成的伪造人脸，并提供更详细的关于篡改方法和所用生成器的标注。除了在我们的基准上评估最先进方法外，我们设计了一种创新的跨外观-边缘学习（CAEL）检测器，以捕获多粒度的外观和边缘全局表征，并检测具有区分性和通用性的伪造痕迹。此外，我们设计了一个外观-边缘交叉注意力（AECA）模块，以探索两个领域间的多种融合方式。大量实验结果和可视化表明，我们的检测模型在跨生成器、跨伪造方法和跨数据集评估等不同设置下均优于现有技术。代码和数据集将在 \url{https://github.com/Jenine-321/GenFace} 提供。