Validating Deepfake-Resistant Identity Solutions
A customer needed to evaluate multiple deepfake detection, biometric authentication, and document verification solutions - safely, quickly, and without exposing sensitive data.
Outcomes
80%
Reduction in Fraudent Attempts
25%
Manual Review Decrease
<0.5%
False Acceptance Rate
99.5%
True Acceptance Rate
Technology Vendors Suited to Evaluation
Business Problem
Challenges
- Rising GenAI abuse: The volume and sophistication of AI-generated deepfakes made it difficult to verify identities in real time.
- Integrity of onboarding: Fraudsters were submitting deepfakes during customer onboarding, leading to potential financial losses and erosion of public trust in digital identity systems.
- Operational strain: Manual reviewers were overwhelmed by complex fraud cases, increasing operational costs, delays, and the risk of human error.
From Idea to Evidence with NayaOne
A customer needed a safe, repeatable way to evaluate deepfake detection, biometric authentication, and document verification tools without exposing sensitive data.
They focused on three key areas:
- Detection performance: Accuracy and resilience of each solution across devices, bandwidth, and demographics.
- Operational fit: Integration into onboarding flows, measuring latency, error handling, and reviewer efficiency.
- Governance and assurance: Full auditability, mapped controls, and evidence packs for clear comparison.
Through the NayaOne platform, vendors were deployed inside a secure, air-gapped sandbox using synthetic video, voice, and document data to simulate real fraud patterns without exposing live PII. All testing ran under pre-configured security, audit, and governance controls. Each solution was evaluated in real and near-real time for liveness, spoofing, face match, and document tamper detection, then integrated into a reference onboarding flow to capture latency, throughput, and manual escalation processes. All test runs were logged for audit and consolidated into evidence packs comparing accuracy, performance, and compliance readiness.
Impact Metrics
PoC Timeline Reduction
6 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
10 - 16 months
Decision Quality
Continuous evaluation with contextual risk validation and a bank-native deployment path.
KPIs
- Detection Accuracy – Validated true and false acceptance rates to confirm >95% precision against deepfakes and spoof attempts.
- Attack Resilience – Tested across face swaps, voice clones, and replays to prove robustness under real-world attack scenarios.
- Latency – Measured average detection time under 800 ms to maintain real-time onboarding performance.
- Manual Review Rate – Tracked and reduced human escalations to below 15% through better automation and workflows.
- Audit Readiness – Logged every test for full traceability and compliance with industry standards.
Validate Deepfake-Resistant Identity Solutions
Compare and test biometric, document-verification, and deepfake-detection tools in a secure sandbox to assess real-world performance and regulatory readiness.
