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You cannot reliably confirm from a video-call image alone that the face belongs to a live person—or that the person is who they claim to be. A replay attack is a photo or video of a face presented to a camera; biometric presentation-attack detection (PAD) is designed to identify such attacks during biometric capture. NIST’s replay figures come from an offline evaluation of archived imagery, not a test of ordinary video calls.
What a replay attack and liveness detection mean
NIST defines presentation-attack detection as the automated determination of a presentation attack. A replay is one kind: an attacker holds a photo or video of a face to the camera. Liveness detection is a subset of PAD that measures anatomical characteristics or voluntary or involuntary reactions to assess whether a living subject is present at capture. NIST CSRC glossary NIST FATE PAD program
These terms describe checks within a biometric capture system. They do not mean that a person watching a call can reliably authenticate someone by spotting a blink, head movement, or other informal visual cue.
What NIST’s replay numbers actually measure
NISTIR 8491, published in September 2023, evaluated 82 prototype software algorithms from 45 developers using archived conventional 2D face imagery. The evaluation was passive and offline. It did not test 3D, near-infrared, hardware approaches, real-time interaction, or challenge-response. NISTIR 8491
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The report uses two error measures. APCER is the proportion of attack presentations incorrectly accepted as bona fide; BPCER is the proportion of bona fide presentations incorrectly called attacks. The figures below are selected best results in the benchmark at specified operating points, not universal error rates.
| NIST test category | Convenience-focused result | Security-focused result |
|---|---|---|
| Still-image impersonation detection, photo print/replay (PA Type 8) | Best listed APCER: 0.0 at BPCER = 0.01. This means the benchmark’s reported missed-attack proportion at a setting limiting false attack flags on bona fide imagery to 1%. | Best listed BPCER: 0.0001 at APCER = 0.01. This means the benchmark’s reported false-flag proportion at a setting limiting missed attacks to 1%. |
| Zoomed replay | Best listed APCER: 0.005 at BPCER = 0.01. | Best listed BPCER: 0.006 at APCER = 0.01. |
Here, “0.0” is the value reported at that tested operating point; it does not prove that a system can never miss a replay in other conditions. Changing the threshold can trade missed attacks against false alarms. NISTIR 8491
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Why those figures do not tell you whether a video-call face is real
The evaluation tested PAD algorithms against archived camera imagery and specified presentation attacks. Its results do not establish a pass/fail rate for people appearing in everyday video calls. Call software, compression, lighting, camera quality, and the attack path differ from the benchmark conditions.
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- Real-time synthetic video: a generated or altered face may appear live. The report’s replay results do not establish how well an algorithm detects every synthetic face.
- Software injection: manipulated video may be introduced after camera capture. NISTIR 8491 explicitly does not assess non-presentation attacks that subvert software after capture.
NIST cautions that PAD accuracy varies across algorithms, use cases, and presentation-attack types. Results should be compared only when the attack type, operating point, capture conditions, and evaluation scope are relevant to the system being considered. NISTIR 8491
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What active deepfake checks have shown
A 2022 paper by Hui Guo, Xin Wang, and Siwei Lyu proposed an active-probing idea for video conferencing: briefly display a distinctive pattern, then look for its reflection in the participant’s cornea. In the authors’ experiments, the pattern appeared in real-video corneal reflections but not in the tested DeepFake outputs. The paper describes a research method, not a consumer-ready or foolproof check. Its implementation ran at one frame per four seconds and depended on reliable face, eye/iris, and reflection detection, as well as adequate image resolution. Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.PAD is not identity verification
Even a successful liveness check would address whether a sample appears to come from a live subject at capture—not whether that subject is the person they claim to be. NIST SP 800-63-4 says facial-recognition biometric systems must implement PAD, recommends deployment testing demonstrate an impostor attack presentation accept rate below 0.07, and requires biometrics to be used with a physical authenticator as part of multi-factor authentication. Those provisions apply to biometric authentication systems; they do not mean every ordinary call has PAD or that PAD alone proves identity. NIST SP 800-63-4
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NIST also notes that face images can be obtained without consent and that PAD depends on trust in the sensor and biometric processing. For a high-stakes conversation, verify identity through an independently trusted route, such as a previously established contact method, rather than relying on the face on screen alone. NIST SP 800-63-4
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