  As ultra-realistic face forgery techniques emerge, deepfake detection has
attracted increasing attention due to security concerns. Many detectors cannot
achieve accurate results when detecting unseen manipulations despite excellent
performance on known forgeries. In this paper, we are motivated by the
observation that the discrepancies between real and fake videos are extremely
subtle and localized, and inconsistencies or irregularities can exist in some
critical facial regions across various information domains. To this end, we
propose a novel pipeline, Cross-Domain Local Forensics (XDLF), for more general
deepfake video detection. In the proposed pipeline, a specialized framework is
presented to simultaneously exploit local forgery patterns from space,
frequency, and time domains, thus learning cross-domain features to detect
forgeries. Moreover, the framework leverages four high-level forgery-sensitive
local regions of a human face to guide the model to enhance subtle artifacts
and localize potential anomalies. Extensive experiments on several benchmark
datasets demonstrate the impressive performance of our method, and we achieve
superiority over several state-of-the-art methods on cross-dataset
generalization. We also examined the factors that contribute to its performance
through ablations, which suggests that exploiting cross-domain local
characteristics is a noteworthy direction for developing more general deepfake
detectors.
