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Third-party deepfake detectors analyze the audio or video a meeting platform makes available to them, looking for signs such as visual artifacts, unnatural changes between frames, or a mismatch between speech and lip movement. They can flag suspicious media, but a warning is not proof of identity—and no warning is not proof that a caller is genuine.
What a live-call deepfake detector actually does
A third-party detector is an application or service that receives some meeting media, analyzes it with one or more models, and reports a result—often as a risk score or warning. “Third-party” means the detector is supplied separately from the meeting platform’s own call features. Depending on the integration, a bot may join the call or an app may subscribe to participant media streams.
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The detector’s view is limited to the data it can access. It may receive a particular participant’s video, audio, or both; it may inspect only selected time windows; and its findings depend on the quality and completeness of those streams. A score describes what the system inferred from the media it examined, not who is physically present or whether a person is acting freely.
Detection is therefore one signal for follow-up, not an identity credential. A clean result means only that the configured system did not report signs it recognizes in the media it examined. It cannot establish that the video or voice is authentic.
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How the media gets from a meeting to a result
There are two separate parts to many deployments: an integration that obtains and routes meeting media, and a detection model that analyzes it. Platform APIs can enable the first part and provide a place to display the result; they do not necessarily supply or validate the model.
Example: a bot reports a finding in Microsoft Teams
Microsoft’s Microsoft Graph beta documentation describes a reportSyntheticMedia endpoint. A certified third-party bot that has joined a Teams call can report synthetic-media findings for a participant. The tenant administrator must grant the app the Calls.ReportSyntheticMedia.All application permission. The report can then appear in participant roster information.
This endpoint is a reporting mechanism: it transmits an analysis result from a detector. It is not itself evidence that Microsoft performed the analysis or that the detector’s result was independently validated. The beta status also matters to organizations evaluating deployment; an API’s existence does not by itself establish general availability or suitability for a particular production workflow.
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Example: selected media streams in Zoom RTMS
Zoom’s May 22, 2026 developer example demonstrates a different pattern. An app selects a participant, subscribes to that person’s individual video and audio streams, converts the media into short clips, sends those clips to detection services, and presents scores in a Zoom App. This illustrates why integration design affects coverage: the application is analyzing the selected streams, not an abstract, complete view of everything happening in the meeting.
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In that sample, video is sent as two-second clips at five frames per second, and audio is analyzed in rolling four-second windows. These are demonstration settings, not universal requirements or evidence of accuracy. The post says the Hugging Face models are used as-is and have not been fine-tuned or validated for production accuracy in the sample. Models used elsewhere may require different formats, sample rates, frame rates, or durations.
| Integration example | What it illustrates | What it does not establish |
|---|---|---|
| Microsoft Teams beta reporting endpoint | A joined, certified third-party bot can report a participant-level finding after an administrator grants the required application permission. | The endpoint does not perform or validate the detection model’s analysis. |
| Zoom RTMS developer sample, May 2026 | An app can subscribe to individual participant streams, prepare short audio and video inputs, and display model scores in a Zoom App. | The sample’s clip lengths, frame rate, models, and scores are not universal settings or a production-accuracy benchmark. |
These examples show that a detector needs both access to media and a route for returning its result. Which participants and modalities it can inspect depends on platform support, permissions, call configuration, and how the integration is built.
What signals detectors look for
Different systems combine different clues. A single clue can be ambiguous, so combining evidence across modalities or across time may help an analysis—but it does not remove the need to evaluate how the detector performs on the conditions where it will be used.
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A model may examine an individual image for visual artifacts associated with synthesized or altered faces. The useful details can be subtle, and what is visible depends on such conditions as lighting, image resolution, camera angle, and compression. A still frame provides no direct evidence about how the face or scene changes over time.
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Changes across frames
Temporal analysis compares images from different moments in the call. It may look for facial drift, unnatural movement, or inconsistencies that are harder to assess in one frame alone. Its opportunity to detect those clues depends on receiving enough usable frames; dropped frames, low resolution, or an integration that samples sparsely can reduce the evidence available.
Audio and lip synchronization
A detector with access to both modalities can compare speech with visible mouth movement. A mismatch may be a clue worth investigating, but it is not conclusive on its own: weak audio, poor synchronization, buffering, and incomplete streams can also complicate comparison. A video-only detector cannot make an audio-to-mouth comparison, and an audio-only detector cannot inspect visual artifacts.
Active probing and corneal reflections
A 2022 research preprint by Haiying Guan, James Horan, and Andrew Zhang proposed an active-probing approach: show a distinct pattern and examine its reflection in a participant’s eyes. This differs from passively analyzing whatever the call already contains because it uses a prompted visual response as an additional clue. It depends on adequate image quality and on the relevant facial reflection being visible in the stream. A research proposal is not evidence of a universal or commercially validated solution.
What a detector can miss—and why
Attacks outside the model’s tested coverage
A model can only be evaluated against the genuine and manipulated media used in its testing. New or less-represented manipulation techniques, devices, codecs, lighting conditions, accents, and conferencing compression may produce different results. NIST’s Special Publication 800-63A calls for testing with genuine media and available attack artifacts, and for documenting expected false-positive and false-negative rates. Without that information, a score cannot be interpreted as a known probability that the caller is genuine.
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Media injected before the detector receives it
A detector that analyzes a meeting stream may have no reliable way to prove that the stream came directly from a genuine camera or microphone. Virtual cameras, emulators, or compromised devices can affect the media before it reaches the analysis service. NIST discusses technical controls such as detecting virtual cameras and emulators, as well as sensor authentication or device attestation where appropriate. These are capture-path safeguards, distinct from looking for synthetic artifacts in a received stream.
Poor or incomplete audio and video
Compression, low light, weak audio, dropped frames, and missing modalities can remove or obscure the cues a system needs. A result from a participant’s video stream says nothing about voice characteristics if no audio was analyzed. Likewise, a system cannot use synchronization evidence if it does not receive both audio and video in a form and time window that can be compared.
Unsupported provenance signals
Provenance checks look for supported credentials or watermark signals; they are not the same thing as a general-purpose deepfake classifier. Microsoft’s Azure AI provenance detection overview, last updated August 5, 2026, says its service detects Microsoft AI signals and explains that no supported provenance signal does not mean content was not AI-generated. A missing signal may mean only that the content has no credential or watermark the service recognizes.
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Residual risk and ordinary deception
Even layered presentation-attack controls do not address every case. In its remote identity-proofing context, NIST SP 800-63A says: “However, even these mechanisms are not sufficient to address all possible cases of these kinds of attacks.” Deepfake detection also cannot by itself establish a caller’s intentions, determine whether a real person is being coerced, or rule out social engineering. NIST treats identifying manipulation, coercion, and social engineering as separate concerns in remote proofing.
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How to interpret an alert or a clean result
A detector result should be read in the context of the system that produced it: what media it received, which methods it used, and how it was tested. NIST recommends documenting expected false-positive and false-negative rates and using manual review to address automated decision errors. A false positive can flag genuine media; a false negative can fail to flag manipulated media. Neither type of error can be understood from a score alone unless the provider explains its measurement and evaluation conditions.
- An alert: Treat it as a reason to pause and verify, not as conclusive proof that the participant is an impostor.
- No alert: Treat it as “no recognized signal was reported from the analyzed media,” not as confirmation of identity.
- Missing or degraded media: Ask whether the result is incomplete or uncertain when a stream, modality, or usable signal was unavailable.
Do not let a model score become the sole basis for removing a participant, accusing someone, approving a transaction, or granting access unless the organization has a carefully evaluated process for that decision.
How to verify a consequential request
When a caller asks for a payment, credentials, confidential information, or another sensitive action, use a separate, previously trusted route to verify the request. Do not rely on contact details or instructions supplied during the suspicious call.
- Pause the requested action. Do not transfer money, share a password or code, or disclose sensitive information while the request is unverified.
- End or hold the call if needed. Avoid treating a detector alert—or the absence of one—as the decision itself.
- Contact the person through a known channel. Use a phone number already on file, a corporate directory, or another contact route established before the call.
- Confirm the exact request and context. Ask the person or an authorized colleague to verify the instruction independently, including the recipient, amount, deadline, or access being requested.
- Follow the organization’s escalation process. Report suspicious or unusual requests to the appropriate security, finance, or identity-verification contact.
This out-of-band check addresses a different question from media analysis: whether the real person or organization intended the instruction. It is useful even when a detector has no finding, because synthetic-media detection does not cover every form of impersonation or deception.
Questions organizations should ask before deployment
Procurement should assess the detector as part of a larger identity and security process. The following questions expose where a product’s coverage ends and what staff should do when a result is uncertain.
- Platform access: Which meeting platforms and call configurations are supported? Does the integration require a bot to join, administrator consent, specific permissions, or participant admission?
- Media scope: Does it inspect audio, video, or both? Does it receive an individual participant stream, active-speaker video, or something else? Which participants are included or excluded?
- Evaluation conditions: Which attack artifacts and genuine media were tested? Were codecs, conferencing compression, lighting, devices, accents, and call conditions representative of your users?
- Error rates: What measured false-positive and false-negative rates are available? Who performed the evaluation, on what data, and under what conditions?
- Degraded or missing input: What happens if audio is absent, video is low quality, or the system is uncertain? Does it explicitly label an incomplete analysis rather than silently returning a clean result?
- Decision authority: Are findings advisory, or can the system label, block, or remove a participant? Who reviews a flag and what evidence must they consider?
- Privacy and retention: What media is transmitted or stored, where is it processed, who can access it, and when and how is it deleted?
- Independent verification: What known contact channel should employees use before acting on urgent or sensitive instructions?
These are not secondary implementation details. A detector that performs well on selected clips may still leave gaps if it cannot receive the relevant participant’s media, while strong media coverage does not replace a clear human-review and verification process.
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