DF40: Toward Next-Generation Deepfake Detection

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 ( 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?