Top Deepfake Detection And Voice Fraud Prevention Companies

A few seconds of audio from a podcast, an earnings call or a voicemail greeting is now enough to clone a voice. Fraudsters use those clones to call bank contact centers, reset passwords, approve wire transfers and impersonate executives on video calls. Synthetic faces and injected video streams are being used to pass remote identity checks and job interviews. The tools that create these fakes are cheap and improving every month, and the people on the receiving end of the call usually cannot tell the difference.

Deepfake detection and voice fraud prevention vendors analyze audio, video and images for the artifacts that generative models leave behind, and many now run in real time inside phone systems and meeting platforms. Some focus on contact center fraud and voice clone fraud, others on video calls, hiring or forensic review of submitted media. This is one part of a wider shift we describe in our guide to AI-powered threats, and it overlaps with the human side of security tested by social engineering penetration testing companies.

The ten companies below were selected for the breadth of media they cover, whether they work in real time, how they fit into contact center and collaboration tools, and the industries they serve.

Deepfake Detection Companies At A Glance

CompanyHeadquartersBest ForCore ServicesPindropAtlanta, USABanks, insurers and large contact centersSynthetic voice detection, call risk scoring, caller authentication, meeting deepfake detectionReality DefenderNew York, USAEnterprises needing one detection layer across calls, meetings and filesAudio, video and image deepfake detection via API, call and meeting toolsGetReal SecurityAustin, USASecurity teams protecting executives and high-risk video callsLive call identity verification, forensic media analysis, deepfake response servicesResemble AISan Francisco, USADevelopers and enterprises wanting explainable detection with on-prem optionsMultimodal deepfake detection models, explainability, meeting protection, watermarkingHiyaSeattle, USACarriers and businesses protecting phone channels at network scaleAI voice detection API, spam and fraud analytics, branded callingDaonFairfax, USABanks adding synthetic voice checks to existing biometric authenticationSynthetic voice detection, voice and face biometrics, contact center fraud detectionValidSoftHartford, USAFinancial services and government contact centersVoice deepfake detection, voice biometrics, voice multifactor authenticationSensityAmsterdam, NetherlandsInvestigators, media and law enforcement needing forensic reportsDeepfake detection for video, image and audio, forensic analysis and reportsDuckDuckGooseDelft, NetherlandsIdentity verification providers and KYC onboarding teamsImage and video deepfake detection, speech detection, KYC deepfake checksAttestivLehi, USAInsurers and lenders validating submitted photos, documents and videoDeepfake video and audio detection, fake image detection, document fraud detection

Best Deepfake Detection And Voice Fraud Prevention Companies For 2027

1. Pindrop — Contact Center Deepfake Detection, Risk Scoring And Authentication

Pindrop, based in Atlanta, has worked on phone fraud for over a decade and now sells three engines. Pindrop Pulse detects deepfakes and synthetic voices, with one version for contact centers and another for video meetings. Pindrop Protect scores fraud risk during live interactions, with an optional Fraud Assist add-on for AI-assisted investigations. Pindrop Passport authenticates known callers with less friction, and its VeriCall add-on validates caller ID and detects spoofing.

Pindrop says it is used by 14 of the top 20 banks and five of the top seven life insurers, and named customers include HealthEquity and the Virginia Employment Commission. That scale matters because its models are trained on a very large volume of real call audio. It also integrates with contact center platforms such as Zoom Contact Center. For financial institutions where voice clone fraud is already hitting the IVR and the agent queue, it is the most established option on this list.

2. Reality Defender — Multimodal Detection For Calls, Meetings And Uploaded Media

Reality Defender is a New York company founded in 2021 by Ben Colman and backed by Y Combinator. It detects manipulated audio, video and images through several products. RealScan offers browser-based review with no integration, RealAPI returns analysis inside your own applications, RealCall detects synthetic speech in IVR and live calls, and RealMeeting analyzes audio and video during virtual meetings. Developers can start with a free API key and documentation.

Its customer base spans financial services, telecommunications, government, insurance, healthcare payers, staffing and identity verification providers. Use cases include contact center fraud, executive impersonation, hiring fraud, account recovery and media verification. The range of entry points is the main draw: a fraud team can start with manual file review and move to real-time call screening without switching vendors. It suits organizations that need deepfake detection across more than one channel and want to embed it in their own workflows.

3. GetReal Security — Continuous Identity Verification On Live Video And Voice Calls

GetReal Security grew out of work by UC Berkeley professor Hany Farid, a long-time researcher in digital forensics, and was co-founded by Ted Schlein of Ballistic Ventures. GetReal Protect verifies identity during live video and voice calls, and in May 2026 the company made continuous identity verification generally available, combining impersonation detection and deepfake detection across voice and video. It integrates with Microsoft Teams, Cisco Webex and Zoom and has more than 40 identity integrations, including Okta, Microsoft Entra and CyberArk.

GetReal Inspect handles forensic verification of video, voice and image files, and the Prepare and Respond services give access to synthetic media experts during an incident. Protect flags when a participant’s face no longer matches the enrolled identity, when synthetic speech is introduced, or when face swapping is detected. That combination fits enterprises worried about executive impersonation on video calls, such as fake CFO payment requests, and teams that need defensible forensic analysis after an event.

4. Resemble AI — Explainable Detection Models With Air-Gapped Deployment

Resemble AI started in 2019 as a voice generation company and now sells Resemble DETECT, a multimodal detection product for audio, video and images. Its DETECT models analyze raw signals frame by frame and look for artifacts from generative architectures, so they do not need prior voice or face samples from the person being checked. Resemble Intelligence adds human-readable explanations of which artifacts drove a score, plus speaker profiling and exportable audit trails.

Detection runs in real time inside Zoom, Teams, Meet, Webex and phone systems, and developers can reach it through a REST API, Python and Node SDKs or MCP for coding tools. Deployment options include cloud, on-premises and air-gapped installs, with a zero retention mode that deletes media after analysis. Having built voice cloning itself, the company knows how fakes are made. It is a good fit for regulated buyers who need deepfake detection they can host themselves and explain to auditors.

5. Hiya — Real-Time AI Voice Detection For Carriers And Enterprises

Hiya is a Seattle company founded in 2016 that protects phone calls for mobile operators, businesses and consumers. In 2024 it acquired Loccus.ai, a startup focused on detecting AI-generated voices, and now sells that technology as Hiya AI Voice Detection. The detector needs about 1.5 seconds of audio to identify a synthetic voice, works across languages and formats, and is available through an API that businesses and carriers can build into their own systems.

Hiya combines voice detection with its Spam Analytics product, which stops spam and fraud on mobile networks, and Hiya Connect, which shows a business’s branded caller ID on outbound calls. Its products serve more than 500 million users across the Hiya Network. That network view is the main reason to choose it: Hiya sees fraud patterns across huge call volumes rather than one company’s traffic. Telecom operators and businesses that run large outbound or inbound phone programs will benefit most.

6. Daon — Synthetic Voice Detection Alongside IdentityX Biometrics

Daon is an identity company with US headquarters in Fairfax, Virginia, and operations in Dublin, Ireland. Its IdentityX platform handles biometric authentication with face, voice and fingerprint, and its AI.X family adds deepfake defense. The xDeTECH product detects synthetic voices in real time: seconds after a caller starts speaking, it looks for cues that the voice is generated and produces a fraud signal the contact center can act on.

xDeTECH needs no customer enrollment, works regardless of language or dialect, and does not require biometrics or personal data, so it can run on every call rather than only enrolled customers. Daon’s voice biometrics are also available for Genesys Cloud CX. The company’s strength is combining identity verification and synthetic voice detection in one vendor, which suits banks and telecoms that already authenticate callers by voice and need to make sure the voice is real.

7. ValidSoft — Enrollment-Free Voice Deepfake Detection On Every Call

ValidSoft builds voice authentication and fraud prevention technology for financial services and government organizations and lists its office at 100 Pearl Street in Hartford, Connecticut. Its Voice Verity product detects AI-generated voices in real time on every call and every channel, with no customer enrollment required. VoiceID confirms who the speaker is, and VoiceMFA adds voice as a factor in multifactor authentication.

The company has a long history in telecom-based authentication and transaction verification and has received European Privacy Seals for several products, which helps with procurement at privacy-conscious banks. Separating deepfake detection from identity verification is useful: Voice Verity can run on all inbound calls to catch voice clone fraud before any authentication step, while VoiceID handles known customers. It is a good fit for institutions that want voice-specific tools from a vendor focused on that one channel.

8. Sensity — Forensic Deepfake Analysis With On-Premise Deployment

Sensity is an Amsterdam company led by co-founder and CEO Giorgio Patrini, and one of the earliest firms to track deepfakes at scale. Its platform detects manipulated and synthetic video, images and audio and produces forensic reports that explain the findings. Access is through an API, an SDK or a web app, and it can be deployed in the cloud or on premises for organizations that cannot upload sensitive evidence.

Sensity has worked with news organizations, social platforms, brands and law enforcement, and in 2026 it received European Innovation Council funding to build forensics-focused detection models. The forensic emphasis is what separates it from call-center tools: the output is meant to stand up in an investigation, not just trigger an alert. It suits fraud investigation units, insurers reviewing claims evidence, newsrooms and public sector bodies that need documented analysis of a specific file.

9. DuckDuckGoose — Explainable Deepfake Checks Built Into KYC Onboarding

DuckDuckGoose is a Delft company founded by Parya Lotfi, Joris Mollinga and Mark Evenblij. It detects manipulated images, video and voice and produces explainable results that compliance teams can review and audit. DeepDetector handles images and video and can run on premises, Phocus is a web platform where fraud, compliance and legal teams review media and export an audit trail, and Waver detects deepfake speech in more than 16 languages.

Its main focus is identity verification. DuckDuckGoose adds a deepfake check to KYC flows to catch synthetic faces, AI-generated ID documents and injected or replayed video during onboarding, and it also offers document verification and morphing attack detection through SDKs and APIs. Named customers have included the Dutch House of Representatives and identity verification firm DataChecker. Banks, fintechs and identity providers fighting synthetic identity fraud at signup are the natural buyers.

10. Attestiv — Media Validation For Insurance Claims And Underwriting

Attestiv validates digital media before it influences a high-risk decision, and lists its office in Lehi, Utah. Its DeepScan platform checks submitted files, with separate products for deepfake video and audio detection, fake image detection and document fraud detection. An API and platform let teams build validation into claims, underwriting and onboarding workflows instead of reviewing files by hand.

The company has a dedicated insurance offering for validating claim and underwriting photos, documents, audio and video. That matters because generative tools make it easy to fabricate damage photos, receipts and repair estimates. Rather than screening live calls, Attestiv focuses on the files customers upload, which is where much insurance and lending fraud happens. It is the right fit for insurers, lenders and other businesses that make payouts or approvals based on submitted media.

How To Choose A Deepfake Detection Provider

Deepfake detection products look similar in a demo. These questions separate them in production.

Which Channel Is Under Attack: Phone, Video Calls Or Uploaded Files?

Contact center fraud needs real-time synthetic voice detection inside your telephony stack, which is where Pindrop, Daon, ValidSoft and Hiya focus. Executive impersonation on video calls needs meeting integrations, as offered by GetReal, Reality Defender and Resemble AI. Fraud in claims and onboarding is about files and KYC streams, where Attestiv, Sensity and DuckDuckGoose are stronger.

Does Detection Require Enrollment Of Voices Or Faces?

Biometric authentication only protects customers who have enrolled. Artifact-based detection works on any caller or participant, which is what you need to catch voice clone fraud against new customers or impersonation of people outside your company. Many teams use both layers.

How Does The Vendor Keep Up With New Generators?

New voice and video models appear constantly. Ask how often detection models are retrained, how many generators they are tested against, and how quickly the vendor added coverage for the last major release. Request results on your own call recordings rather than relying on published accuracy figures.

Can It Run In Our Environment And Explain Its Results?

Regulated buyers may need on-premises or air-gapped deployment and zero data retention. Fraud and legal teams also need an explanation they can act on, not just a score. Check whether the product produces audit trails and reports suitable for investigations.

What Happens After A Deepfake Is Flagged?

Detection is only useful if it triggers the right response: step-up verification, a call transfer, a hold on a payment or an escalation. Make sure alerts reach your fraud and security teams, and fold deepfake scenarios into your plans with your incident response provider.

Conclusion

Voice clone fraud and deepfake video have moved from proof of concept to routine fraud tools. The companies on this list attack the problem from different angles: real-time voice fraud prevention for contact centers, identity checks for video calls, and forensic review of uploaded media. Map where your organization is most exposed, test two or three vendors on your own data, and pair detection with clear procedures so a flagged call or file leads to a fast, consistent response.