  We propose a new comprehensive benchmark to revolutionize the current
deepfake detection field to the next generation. Predominantly, existing works
identify top-notch detection algorithms and models by adhering to the common
practice: training detectors on one specific dataset (e.g., FF++) and testing
them on other prevalent deepfake datasets. This protocol is often regarded as a
"golden compass" for navigating SoTA detectors. But can these stand-out
"winners" be truly applied to tackle the myriad of realistic and diverse
deepfakes lurking in the real world? If not, what underlying factors contribute
to this gap? In this work, we found the dataset (both train and test) can be
the "primary culprit" due to: (1) forgery diversity: Deepfake techniques are
commonly referred to as both face forgery (face-swapping and face-reenactment)
and entire image synthesis (AIGC). Most existing datasets only contain partial
types, with limited forgery methods implemented; (2) forgery realism: The
dominant training dataset, FF++, contains old forgery techniques from the past
five years. "Honing skills" on these forgeries makes it difficult to guarantee
effective detection of nowadays' SoTA deepfakes; (3) evaluation protocol: Most
detection works perform evaluations on one type, e.g., train and test on
face-swapping only, which hinders the development of universal deepfake
detectors. To address this dilemma, we construct a highly diverse and
large-scale deepfake dataset called DF40, which comprises 40 distinct deepfake
techniques. We then conduct comprehensive evaluations using 4 standard
evaluation protocols and 7 representative detectors, resulting in over 2,000
evaluations. Through these evaluations, we analyze from various perspectives,
leading to 12 new insightful findings contributing to the field. We also open
up 5 valuable yet previously underexplored research questions to inspire future
works.
