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.
Incode’s identity verification system exceeds all expectations.
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.
Time it takes to generate a convincing deepfake with free AI tools.
Source: MIT Tech Review, 2023
Human accuracy in spotting deepfakes, barely better than chance.
Source: Cooke et al., 2024
Lost to identity fraud by U.S. banking customers in 2024.
Source: AARP/Javelin, 2024
Increase in fintech deepfake incidentsin 2023.
Source: Deloitte, 2024
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.
Flags anomalies in device motion and user behavior that signal bots or scripted fraud.
Detects tampered, emulated, or rooted devices that allow spoofing or injection.
Blocks virtual cameras and prevents video injection using camera source validation.
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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.
Why Incode Deepsight
Detect deepfakes, injections, and tampered devices with the world’s best deepfake detection system – with accuracy independently validated by Purdue University.
Get ahead of accelerating AI-assisted fraud
Fraud evolves quickly and strikes from multiple angles. Deepsight responds in real time, blocking deepfakes at the behavioral, integrity (device, camera), and perception layers (advanced multi-modal AI to liveness detection).
Threats to your business
Deepfakes and physical spoofs that bypass liveness checks
Virtual camera and manipulated video feeds
Devices running in emulators or tampered environments
Bots and automation that mimic real users
Adding new fraud detection layers can increase friction and overheads
Deepsight’s Solution
Detects deepfakes and spoofs with multi-modal analysis across video, motion, and depth.
Identifies virtual camera feeds with camera source validation
Detects tampered devices and emulators with device integrity checks
Flags automated behavior and high-frequency bot activity in real time
Provides frictionless protection, invisible to the end-user and not affecting verification speed
Adapting to fraud at the speed of AI
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.
Where deepfakes break digital trust
with a Gen AI deepfake
Fraudsters use deepfake selfies and videos to bypass biometric verification.
at the helpdesk
Attackers fool helpdesk agents with fake identities or manipulated videos.
Customers and industry thought leaders trust Incode
In-house testing against a representative sample of over 1350 deepfakes
better false-positive rate than the next-best commercial technology
surpassing human labelers across every test.
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