Topic starter
11/11/2026 6:00 pm
TL;DR: Injection attacks rose 200% relative to deepfakes, and Mitek Systems' study says 70% of advanced fraud cases needed an additional screening layer. Biometric fraud controls now need transit integrity and comparison-layer analysis, not just capture checks.
Editorial analysis by NHI Mgmt Group, based on content published by Mitek Systems: “Designing for injection: What fraud systems must do now”.
By the numbers:
- Injection attacks increased 200% relative to deepfakes by the end of 2025.
- 70% of advanced fraud cases required an additional screening layer beyond traditional biometric capture protections.
Key questions
Q: Why do liveness checks fail against replay and deepfake fraud?
A: Liveness checks fail when the attacker can present a convincing recording, synthetic face, or injected video that satisfies the challenge without a live person being present.
Q: What should teams do first when biometric injection is suspected?
A: First, stop the transaction or enrolment flow and isolate the session or device linked to the biometric event.
Q: Why do injection attacks defeat mobile biometric verification?
A: They defeat it because the attacker does not need to spoof the face in front of the camera.
Practitioner guidance
- Implement capture, transit, and comparison as separate control layers Design biometric onboarding so each layer checks a different failure mode and each layer can still operate even when another layer reports success.
- Detect stream manipulation in real time Monitor for replayed content, proxy interference, emulator environments, driver tampering, and state changes between capture and decisioning.
- Correlate fraud signals across layers Feed anomalies from liveness, document analysis, transit telemetry, and comparison profiling into one investigation path so suspicious events reinforce each other.
Bottom line: Injection attacks exploit the gap between biometric capture and backend decisioning, which makes the data path a primary fraud control surface.
Glossary terms: Injection attack, Comparison Layer, Liveness Detection, Biometric Onboarding
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11/11/2026 6:07 pm
Injection defense is a data-path governance problem, not a camera problem. Fraud programmes that only validate what appears in front of the lens assume the attack ends at capture. In practice, injection attacks exploit the handoff between capture, transport, and comparison, which means the real control surface is the integrity of the identity evidence pipeline. Practitioners should govern the whole path, not the sensor alone.
A question worth separating out:
Q: How can fraud teams detect manipulation between capture and matching?
A: They should look for tampering in the path between image or video capture and the matcher, including frame substitution, metadata changes, API interception, and client-side injection. Integrity checks, signed capture payloads, secure transport, device attestation, and audit logs help expose manipulation before the biometric sample reaches matching.
👉 Read our full editorial: Injection attacks on biometric fraud now demand layered controls