A new model detects deepfakes without needing any examples

Unlike FACTOR, most current methods train on available deepfakes, but fail to generalize to new, unseen ones

FACTOR leverages discrepancies between false facts and their imperfect synthesis with deepfakes

Deepfakes are a growing concern in our digital discourse, with the potential to further undermine trust online. As these AI-generated forgeries become more convincing, detecting them becomes increasingly challenging. Traditional methods falter, trained on datasets of known fakes, they struggle to identify new, unseen manipulations.

A new paper presents a fresh perspective. The authors introduce FACTOR, a detection method that shifts the focus from what's seen to what's claimed. Instead of looking for visual or auditory signals of tampering, FACTOR evaluates the consistency of the media with the real-world facts it represents. It questions if the person depicted is who the media claims they are, or if their words match their known voice or stance.

FACTOR stands out because it doesn't need a catalog of fakes to learn from. It performs a fact-checking operation, similar to methods used to spot fake news. How well does this approach work? What are its limitations? Let's find out.

The Deepfake Detection Problem

Deepfakes leverage deep learning, especially generative adversarial networks (GANs), to manipulate media like images, video, and audio. Common tactics include face-swapping, puppeteering likenesses, and synthesizing speech. While some uses are harmless fun, deepfakes can also enable malicious disinformation campaigns, spoof identities, and violate privacy.

As generative methods advance rapidly, new deepfake techniques continually emerge. Classical detection methods relying on artifacts fail against modern fakes. Current state-of-the-art uses classifiers trained to discriminate real from fake media. But these supervised models only catch deepfakes similar to their training data. They cannot generalize to new, "zero-day" attacks - a major drawback.

Attackers often implicitly or explicitly make false claims alongside fakes, such as about a person's identity, speech, or appearance. This motivates verifying these claimed "facts". If current AI cannot perfectly encode false facts into deepfakes, checking for inconsistencies can reveal fakery.