Cantivia detects deepfakes and disinformation, and simulates how synthetic media spreads, so platforms, newsrooms and fraud teams know what's fake, who made it, and how far it'll travel.
Cantivia detects deepfakes and disinformation, and simulates how synthetic media spreads, so platforms, newsrooms and fraud teams know what's fake, who made it, and how far it'll travel.
Project Details
Updated 07/06/26 · Provided via application · VerifiedCantivia is an AI-security company tackling synthetic media end to end. Praetorian detects deepfakes across image, audio and video and fingerprints which model generated them, using DCT frequency-domain analysis on a custom backbone. Our second tool flags disinformation at 99% accuracy, built by retraining layers of a 30B model, which we use to verify the truth. SEDPNR, a 10,000-agent simulated population built from survey data on 1,000+ real people, models how a piece of media spreads amongst an artificial population. We serve platforms, newsrooms and fraud/trust-and-safety teams who need to know what's fake, who made it, and how far it will travel. I (Ashkan) lead Praetorian; my co-founders Feargal and Oisin lead the disinformation classifier/SEDPNR and operations/go-to-market respectively. We've been building together since January 2026 and met through school 3 years ago.
Theory of Impact
Updated 07/06/26 · By grantmaking.aiAs generative AI gets cheaper and better, most of what people see, hear and read online will be synthetic, so verifying what's real, who made it, and how it spreads becomes essential infrastructure. Detection hasn't kept up: it's slow, single-modality, offers no attribution, and no spread modelling. Cantivia builds that trust layer across all three, giving platforms, newsrooms and fraud teams a way to catch fakes, trace them to source, and anticipate their reach before they cause harm. This strengthens the human/machine boundary in an information environment that is degrading as synthetic content scales.
People
Updated 07/06/26 · By grantmaking.aiTeam Member
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