  Deepfake attacks, malicious manipulation of media containing people, are a
serious concern for society. Conventional deepfake detection methods train
supervised classifiers to distinguish real media from previously encountered
deepfakes. Such techniques can only detect deepfakes similar to those
previously seen, but not zero-day (previously unseen) attack types. As current
deepfake generation techniques are changing at a breathtaking pace, new attack
types are proposed frequently, making this a major issue. Our main observations
are that: i) in many effective deepfake attacks, the fake media must be
accompanied by false facts i.e. claims about the identity, speech, motion, or
appearance of the person. For instance, when impersonating Obama, the attacker
explicitly or implicitly claims that the fake media show Obama; ii) current
generative techniques cannot perfectly synthesize the false facts claimed by
the attacker. We therefore introduce the concept of "fact checking", adapted
from fake news detection, for detecting zero-day deepfake attacks. Fact
checking verifies that the claimed facts (e.g. identity is Obama), agree with
the observed media (e.g. is the face really Obama's?), and thus can
differentiate between real and fake media. Consequently, we introduce FACTOR, a
practical recipe for deepfake fact checking and demonstrate its power in
critical attack settings: face swapping and audio-visual synthesis. Although it
is training-free, relies exclusively on off-the-shelf features, is very easy to
implement, and does not see any deepfakes, it achieves better than
state-of-the-art accuracy.
