The world’s most accurate deepfake detection system

Deepsight protects organizations from deepfakes, AI-driven impersonation, synthetic document fraud, camera injections, and device tampering with unmatched accuracy, because when identity can be faked, everything breaks.

Customers and industry thought leaders trust Incode

Independent studies, customer stories and verified reviews validate the impact of Incode’s technology.

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Validated as the most accurate deepfake detector

“We evaluated nine of the most widely used commercial deepfake detection systems and found that Incode’s detector achieved the highest accuracy in identifying fake samples, yielding the lowest false acceptance rate.”

— Shu Hu, Assistant Professor & Director, Purdue Machine Learning Lab

Ranked as the top-performing system in the benchmark across government, academic, and commercial detectors.

Benefits

Why Incode Deepsight

Proven Deepfake defense system

Detect deepfakes, injections, and tampered devices with the world’s best deepfake detection system – with accuracy independently validated by Purdue University.

Protects against financial losses

Even a single attack can cause major financial and reputational damage. Using a multi-modal AI to stop sophisticated AI-fraud without impacting performance, Deepsight blocks costly threats before they succeed.

Instant activation, zero maintenance

Enable enterprise-grade protection without slowing down your team. Deepsight integrates seamlessly with your IDV process and provides automatic updates through the Incode Trust Platform.

Deepfake attacks are increasing faster than legacy defenses can keep up

Generative AI now makes deepfakes easy to create, cheap to scale, and nearly impossible for humans or traditional systems to detect, creating an existential threat to digital trust.

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Time it takes to generate a convincing deepfake with free AI tools.

50-59% Human accuracy in spotting deepfakes, barely better than chance.

$47B Lost to identity fraud by U.S. banking customers in 2024.

700% Growth Increase in fintech deepfake incidents in 2023.

What is Deepsight

Defend verification journeys against deepfakes from start to finish

Incode Deepsight is a multi-layered fraud detection system that blocks attacks across every critical verification touchpoint. By analyzing behavioral signals, device and camera integrity, the perception layer, and document authenticity in real time, it ensures only real people with real documents are verified, stopping AI-driven identity fraud in its tracks.

Perception Layer

Detects deepfakes by using a large multi-modal AI (video, motion, and depth) and Vision Language Model that identifies document inconsistencies typical of Gen AI tools.

Deepsight for documents

AI-generated documents are the next frontier of identity fraud

Generative AI doesn't just create convincing fake faces — it creates convincing synthetic documents. Deepsight for Documents protects the document layer, catching forged IDs, passports, and supporting documents that traditional verification tools miss.

Its AI forgery detection feature identifies documents created or altered by generative AI tools by detecting visual artifacts, font inconsistencies, and layout anomalies invisible to the human eye.

10x increase in AI generated document fraud over 2 years

8.8× more fraud caught by Deepsight for Documents than document based check alone.

25% of all identity fraud attempts are now AI-assisted and is estimated to grow to 50% by end of year.

Frequently Asked Questions

What is Incode Deepsight?

Deepsight is Incode's proprietary deepfake and liveness detection engine. It uses a multi-layer AI model to identify AI-generated faces, video injection attacks, and presentation attacks in real time — validated by Purdue University as the most accurate system in its class.

How accurate is Deepsight compared to other deepfake detection tools?

Deepsight achieves a 68x better false-positive rate in identity verification compared to the next-best commercial technology, independently validated by Purdue University's Machine Learning Lab.