  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
