Incode announced a research collaboration with Purdue University aimed at deepfake detection and evaluation in real-world identity verification.
Incode Partners with Purdue University for Deepfake Detection Research ↗
What happened
- The partnership pairs Incode's machine learning team with Purdue researchers on five shared goals, including detection of unseen synthetic media and independent evaluation.
- Both teams plan to publish findings and resources openly when they are ready.
- Incode states it won both tracks of the IJCB PAD-ID Card competition and first place in the video category of LivDet-Face 2026.
Why it matters to Signicat
- Signicat's document IDV and fraud offerings face a competitor with publicized independent benchmark wins and an academic research tie-in; relevant to competitive positioning on injection and deepfake detection.
Evidence from the source (4 quotes)
- “The partnership pairs Incode’s machine learning team with Purdue researchers on five shared goals:” source ↗
- “Both teams plan to publish findings and resources openly when they are ready” source ↗
- “In 2026, Incode won both tracks of the IJCB PAD-ID Card document forgery detection competition and took first place in the video category of LivDet-Face 2026” source ↗
- “Incode won both tracks of the IJCB PAD-ID Card document forgery detection competition” source ↗
Fraud deepfake injectionDocument IDV biometricsPartnershipResearch reportGlobal
4 of 4 facts verified against the source · confidence medium · claude-haiku-5-5