Topic starter
11/11/2026 6:05 pm
TL;DR: 1,740% is how much deepfake fraud increased in North America between 2022 and 2023, according to Mitek Systems' survey and case work. Identity verification controls need layered authenticity checks because capture-stage synthetic media can bypass systems built for forged documents and stolen credentials.
Editorial analysis by NHI Mgmt Group, based on content published by Mitek Systems: “Deepfake fraud is scaling faster than defenses”.
By the numbers:
- Deepfakes were 55% higher than injection attacks in early 2025.
- Mitek Systems says deepfake fraud could increase by 700% more by 2031.
- Mitek Systems cites Deloitte projections that fraud losses from generative AI could rise from $12.3 billion in 2023 to $40 billion by 2027.
Key questions
Q: Why do liveness checks fail against synthetic media?
A: Liveness checks fail against synthetic media because the attacker can present a fake face, voice, or motion signal that matches the challenge well enough to satisfy the verifier.
Q: What should teams do first when deepfake fraud is targeting identity verification?
A: Start with the highest-risk recovery and escalation paths, because those flows often accept weaker proof than initial login.
Q: Why do deepfakes change identity verification requirements?
A: Deepfakes change requirements because they let attackers create believable but false evidence that can pass weak visual checks.
Practitioner guidance
- Implement layered identity verification Combine passive liveness, document verification, behavioural analytics and biometric checks so one evasion path cannot satisfy the entire process.
- Add out-of-band approval for high-risk transactions Require a separate callback or second channel before wire transfers, account changes or executive-driven payment requests are approved.
- Test verification flows against synthetic media Exercise onboarding and payment journeys with deepfake video, audio and image scenarios to find which controls fail at capture time.
Bottom line: Deepfake fraud is now exploiting the assumption that captured media is real, which makes traditional identity verification controls insufficient on their own.
Glossary terms: Deep Fake Fraud, Liveness Detection, Passive Liveness, Biometric Verification
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11/11/2026 6:07 pm
Deepfake fraud is a capture problem before it is a detection problem. The decisive weakness is not that organisations lack more analytics, but that many verification stacks still assume the input is a real human signal. Once synthetic media reaches the capture layer, the control question changes from identity proofing to media authenticity. Practitioners should therefore measure where trust is first established, not where fraud is finally detected.
A few things that frame the scale:
- Businesses report a 200% surge in attempted deepfake-aided wire fraud in Q1 2025 alone.
A question worth separating out:
Q: What is the difference between presentation attacks and injection attacks in deepfake fraud?
A: Presentation attacks try to fool the system directly with an image, mask, screen replay, or video shown to the camera. Injection attacks bypass the camera path altogether by replacing the live feed with synthetic or prerecorded media. Both aim to impersonate a real person, but the technical control point and detection approach are different.
👉 Read our full editorial: Deepfake fraud can bypass identity checks at capture time