Deepfake detection tools analyze voice, video and images for signs of AI generation, and the leading ones now run inside live calls and meetings. None is 100% accurate, and none can tell you whether an employee would pause and call back before releasing a payment. Detection is a signal, not a control: the decision is still human.
Key takeaways
- Reality Defender, Pindrop, Sensity, Resemble AI and Intel FakeCatcher cover different modalities and deployments: live meetings, contact centers, forensic review or developer APIs.
- Academic testing on real-world deepfakes found open-source detectors lost roughly half their accuracy outside the lab. Vendor accuracy figures come without published methodology.
- A detector flags a file or a call. It does not verify the request, and it does not train the person who receives it.
- Detection and simulation answer two different questions: "Is this synthetic?" and "Would my team verify before acting?" Mature programs need both.
What are deepfake detection tools, and how do they work?
Deepfake detection tools are software that estimate whether audio, video or an image was generated or altered by AI. They look for artifacts a human would miss: spectral patterns in synthetic speech, pixel-level inconsistencies, lip movements that drift from the audio, or missing physiological signals such as blood flow in the face. Most return a probability score, not a verdict.
They are deployed in four main ways:
- File analysis: upload a recording or image to a web app for review. Common in fraud investigations and digital forensics.
- API integration: send media from your own application, for example during customer onboarding or identity verification.
- Live call screening: real-time scoring of voice calls, often in contact centers.
- Meeting protection: alerts inside video conferencing platforms when a participant's video or voice shows signs of manipulation.
How did we select these five tools?
We chose five tools that security leaders meet most often when they research the category, covering each deployment model above. For each, we used only what the vendor publishes or what reputable press reported, as of October 2026. We did not test the products ourselves, and we do not rank them: the right choice depends on where your exposure sits.
Pricing is left out on purpose. None of the product pages we reviewed publishes it, so treat every tool below as "contact sales".
1. Reality Defender: multi-modal detection across calls, meetings and files
Reality Defender is a detection platform covering audio, video and images, delivered through several products built for different entry points. It is the closest thing the category has to a general-purpose enterprise layer.
- What it detects: synthesis or editing in live and recorded speech, generated or manipulated faces and frames in video, and generated or altered images.
- Deployment: RealScan (browser-based file analysis), RealAPI (programmatic analysis from your own apps), RealCall (cloned voices and AI callers on live calls) and RealMeeting (alerts to hosts during meetings).
- Best for: organizations that want one vendor across contact center, meetings and investigation workflows.
Reality Defender says it was named a Market Shaper in Gartner's June 2026 deepfake detection research. It publishes no accuracy or false-positive rates.
Learn more about Reality Defender →
2. Pindrop Pulse: voice-first detection, extended to video meetings
Pindrop comes from voice authentication and contact center fraud, and its deepfake detection grew out of that base. Pulse for Meetings brings the same analysis into video calls.
- What it detects: synthetic voice patterns on calls; in meetings, visual manipulation such as inconsistent lighting, shifting facial features and lip movements that do not match speech, combined with audio and location signals.
- Deployment: contact center integrations, and Pulse for Meetings on Zoom, Microsoft Teams and Webex.
- Best for: organizations with heavy phone-based customer interaction that also want coverage on internal meetings.
Pindrop reports that its Pulse technology had checked 130 million phone calls for authenticity when the meetings product launched.
3. Sensity AI: forensic-grade analysis for investigations
Sensity AI focuses on proving whether a piece of media is authentic, with outputs designed to stand up as evidence. It suits investigation teams more than live operations.
- What it detects: face replacement and manipulation, AI-generated video and images through pixel analysis, synthetic voices through acoustic and spectral analysis, plus file forensics.
- Deployment: drag-and-drop web app, API and SDK, and on-premise options including a dedicated GPU server and an air-gapped setup.
- Best for: government, legal, fraud investigation and digital forensics teams that need to authenticate media after the fact.
Sensity does not list live meeting coverage and publishes no accuracy metrics.
4. Resemble AI DETECT: developer-friendly detection with real-time options
Resemble AI started in synthetic voice generation and now sells DETECT, a detection product with an API-first design. That background gives it a close view of how generators behave.
- What it detects: synthetic and cloned audio, replay attacks, generated images, face swaps, partial edits, video conferencing deepfakes and AI avatar video.
- Deployment: REST API with Python, Node.js and JavaScript SDKs; real-time analysis that the vendor says runs in parallel inside Zoom, Teams, Meet, Webex and phone systems; on-premise and air-gapped options; a zero-retention mode.
- Best for: product and security engineering teams that want to embed detection in their own workflows.
Resemble AI claims up to 99.5% accuracy for its DETECT-World model, with per-generator figures from 94% to above 99%, and publishes no methodology for these numbers.
Learn more about Resemble AI DETECT →
5. Intel FakeCatcher: physiological signals as a detection method
Intel FakeCatcher, announced in November 2022, takes a different route. Instead of hunting for generation artifacts, it looks for something real video contains and synthetic video usually lacks: blood flow.
- What it detects: the subtle color changes in a face caused by blood moving through veins (photoplethysmography, or PPG), read across frames and combined with eye-gaze analysis.
- Deployment: at launch, a server platform on Intel Xeon Scalable processors handling up to 72 simultaneous streams, accessed through a web interface.
- Best for: teams interested in a detection approach that does not depend on knowing the generator.
Intel claimed 96% accuracy and results within milliseconds at launch, figures that have not been independently tested. FakeCatcher is video-only, and its current commercial availability should be confirmed with Intel.
Learn more about Intel FakeCatcher →
Deepfake detection tools compared
| Tool | Modalities | Live calls or meetings | Deployment | Published accuracy | Typical buyer |
|---|---|---|---|---|---|
| Reality Defender | Audio, video, image | Yes (RealCall, RealMeeting) | Web app, API | None on platform page | Enterprise security, fraud |
| Pindrop Pulse | Audio, video, location | Yes (Zoom, Teams, Webex; contact center) | Platform integrations | Not stated in sources reviewed | Contact centers, meeting security |
| Sensity AI | Video, image, audio, file forensics | Not mentioned | Web app, API, SDK, on-premise, air-gapped | None published | Investigations, government, legal |
| Resemble AI DETECT | Audio, image, video | Yes (vendor-stated) | API and SDKs, on-premise, air-gapped | Up to 99.5% (vendor claim) | Engineering and product teams |
| Intel FakeCatcher | Video | Real-time streams | Server platform (2022 launch) | 96% (vendor claim, 2022) | Teams evaluating video-only detection |
What can't deepfake detection tools see?
Detection tools judge media, not decisions. They can flag a likely synthetic face or voice, but they cannot see whether the request makes sense, whether the employee verified it through another channel, or whether a real but compromised account is behind it. The attack succeeds or fails at the moment a person acts.
How accurate are deepfake detectors outside the lab?
Less accurate than their marketing suggests. The Deepfake-Eval-2024 benchmark, built from deepfakes that circulated online in 2024 (45 hours of video, 56.5 hours of audio and 1,975 images across 52 languages), found that open-source detectors lost about 50% of their AUC on video, 48% on audio and 45% on images compared with earlier benchmarks. Commercial models did better, but still fell short of trained forensic analysts (Chandra et al., March 2025).
Every new generator also resets part of the race. A detector trained on yesterday's models has to be retrained for tomorrow's.
What happens when a flag arrives too late, or not at all?
In early 2024, an Arup employee in Hong Kong transferred $25 million after a video call in which the senior colleagues on screen were AI deepfakes (World Economic Forum, February 2025). The employee saw familiar faces and heard familiar voices. What would have stopped the loss was not a better pixel check. It was a callback on a known number before the money moved.
The pattern is common. In a Gartner survey of 302 cybersecurity leaders run from March to May 2025, 62% said their organization had experienced a deepfake attack in the previous 12 months, involving social engineering or the manipulation of automated processes (Gartner, September 2025).
Blind spots to plan for
- Coverage gaps: a personal phone, WhatsApp or an unmanaged meeting platform sits outside most detection deployments.
- Probability, not proof: a 70% score still needs a human to decide what to do with it.
- Alert fatigue: false positives teach people to dismiss warnings.
- Legitimate channels, illegitimate requests: detection says nothing about a real voice making a fraudulent request.
How do deepfake detection and deepfake simulation fit together?
Detection and simulation answer different questions. Detection asks whether a face or voice is synthetic. Simulation asks whether your people verify before acting, whatever the technology says. A program that relies only on the first leaves the final control, human judgment, untested.
Deepfake detection tools tell your systems whether a face or voice is synthetic. They cannot tell you whether your finance controller would have paused, hung up and called back. Arsen tests that human decision, the one that released the funds in every documented deepfake loss to date.
Arsen is not a detection vendor. Arsen runs deepfake video call simulations in Microsoft Teams: employees receive a meeting invitation from an executive, join a realistic call with a deepfake likeness of that executive, and are coached right after the scenario ends. Arsen's team builds the persona, scenario and assets, and the customer approves them. The simulation is sold as an add-on, and results are reported alongside phishing and vishing campaigns.
Learn more about deepfake simulation →
| Deepfake detection | Deepfake simulation | |
|---|---|---|
| Question answered | Is this media synthetic? | Does this person verify before acting? |
| What it protects | Systems and workflows it is plugged into | The decision, on any channel |
| Output | A score or an alert | Behavioral data and targeted coaching |
| Owner | Security engineering, fraud, IT | Security awareness and human risk |
| Main limit | Accuracy drops on new generators; coverage stops at its integrations | Does not block an attack in progress |
Which deepfake detection tool fits your organization?
Start from where a deepfake would hurt you most, then pick the tool built for that entry point.
- High-volume phone channel: Pindrop or Reality Defender's RealCall.
- Executive and finance meetings: Reality Defender's RealMeeting, Pindrop Pulse for Meetings or Resemble DETECT, depending on your meeting platform.
- Investigations and evidence: Sensity AI.
- Detection inside your own product or onboarding flow: Resemble DETECT or Reality Defender's RealAPI.
Whichever you choose, write the response playbook before the first alert: who gets it, what the employee does, and which out-of-band callback confirms a payment or access request. Then test that playbook with people, not only with software.
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Reality Defender, Pindrop Pulse, Sensity AI, Resemble AI DETECT and Intel FakeCatcher are among the most cited. They serve different needs: live meetings and calls, contact centers, forensic investigation or developer integration. There is no single best tool; the right one depends on the channel you need to protect.
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Detectors use machine learning models to look for traces of generation: spectral artifacts in synthetic speech, pixel-level inconsistencies, lip-sync mismatches or missing physiological signals such as blood flow. Most return a probability score that a person or a workflow then acts on.
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Vendors publish figures above 90%, usually without methodology. Independent research on real-world deepfakes found open-source detectors lost roughly half their AUC compared with lab benchmarks, and commercial models still trailed trained forensic analysts. Treat scores as signals, not proof.
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Some can. Pindrop Pulse for Meetings supports Zoom, Microsoft Teams and Webex, Reality Defender offers RealMeeting, and Resemble AI says DETECT runs in Zoom, Teams, Meet and Webex. Check which platforms and licence tiers are covered before you buy.
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Yes. Detection covers only the channels it is plugged into and can miss new generators. In documented losses such as Arup, the money moved because a person acted on the request. Training and simulation test whether employees verify through a second channel before acting.
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No. Arsen runs deepfake video call simulations in Microsoft Teams that test whether employees verify before acting, delivered done-for-you as an add-on and reported alongside phishing and vishing results.
