Open, cited research
The lab.
We publish open benchmarks of the detectors meant to catch deepfakes, measured against the generation methods adversaries actually use and the conditions media really arrives in. The point is simple: show, in public, what detection can and cannot do, so buyers and vendors do not have to take anyone's word for it.
- 14
- detectors benchmarked
- 12
- demographic groups
- 100%
- open methodology
Published in the open, with a permanent DOI you can cite. View the citation
FeaturedDetectors collapse from near-perfect to near-random.
An initial benchmark and robustness re-evaluation of leading open-source deepfake detectors, stratified across generators, platform degradations, and demographic groups.
Open data · permanent DOI
Building a benchmark together?
We collaborate with research groups, standards bodies, and competitions on open benchmark design, evaluation, and sponsorship. If you work on how to measure synthetic media, we would like to talk.
Field notes
Quarterly changes in attacker tooling, in your inbox.
A short briefing on shifts in attacker tooling, four times a year.