The attack that identity verification was built to stop no longer looks the way it used to. For years, the threat was a fraudster holding a printed photo or a mask up to a camera, and liveness detection largely solved it. The threat now is a deepfake fed directly into the verification pipeline, or an entirely synthetic identity assembled by AI, and the numbers show how fast it moved: one vendor reported a 1,151% year-over-year rise in injection attacks, and a 2025 Gartner survey found 62% of organizations had experienced a deepfake attack.
What separates a real deepfake defense from a checkbox is which layers of the attack it can actually see. A tool that only inspects the final selfie image is blind to an injected video stream; a tool that verifies one session in isolation cannot spot a synthetic identity already used elsewhere.
The Four Things a Deepfake Defense Must Catch
Before the vendors, it helps to name the attack layers, because coverage varies sharply across them:
- Presentation attacks: a photo, screen, or mask shown to the camera. Modern liveness handles most of these.
- Injection attacks: a deepfake video fed into the capture channel through a virtual camera or SDK hook, bypassing the lens entirely. This is the fastest-growing vector.
- Synthetic identities: fabricated or AI-generated documents and faces that belong to no real person, built to pass template matching.
- Cross-platform reuse: the same synthetic identity or fraud pattern deployed across many businesses, invisible to any single one.
8 Vendors Detecting Deepfake and Synthetic Identity Fraud
1. AU10TIX
AU10TIX approaches deepfakes and synthetic identities from both ends of the problem: the individual capture and the wider pattern. On the capture side, it applies dedicated deepfake and synthetic-identity detection alongside forensic-level document authentication, catching AI-generated documents that pass simple template matching but fail pixel-level analysis. On the pattern side, its Serial Fraud Monitor identifies coordinated fraud and reused identity assets across sessions and across organizations, the layer that catches a synthetic identity already deployed elsewhere.
That combination matters because synthetic identity fraud is rarely a one-off. Rings generate fabricated identities at scale and spread them across many platforms, so detection that sees only a single session misses the pattern that defines the attack. AU10TIX’s network-level view, built on a heritage of high-volume, high-stakes identity work, is designed to surface exactly that reuse, while its forensic and biometric checks handle the individual deepfake in seconds.
Key features:
- Dedicated deepfake and synthetic-identity detection built into verification, not bolted on
- Forensic document authentication across 5,000-plus document types with pixel-level analysis
- Serial Fraud Monitor for cross-session and cross-organization synthetic-identity reuse
- Fully automated checks returning in seconds to protect conversion at scale
- Biometric and liveness matching paired with document forensics in one flow
2. iProov
iProov is a biometric specialist focused on proving that a real, live human is present, and it has become one of the most recognized names in deepfake and injection-attack defense. Its Genuine Presence Assurance and Dynamic Liveness technologies use a one-time biometric challenge to confirm liveness in real time, and the company reports surpassing one million daily verifications driven by demand for deepfake defenses.
Its standout credential is independent certification: iProov states its Dynamic Liveness was the first to achieve an Ingenium Level 4 evaluation and the CEN/TS 18099 High specification for injection attack detection through an accredited laboratory. For organizations whose primary concern is high-assurance liveness against injected deepfakes, that measurable, third-party-verified performance is a genuine differentiator. iProov also operates a dedicated threat-intelligence unit, the iProov Security Operations Center, that studies live attack attempts across its customer base and feeds what it learns back into detection, so the system adapts as new deepfake and injection techniques emerge rather than relying on a fixed model.
Key features:
- Genuine Presence Assurance and Dynamic Liveness for real-time human verification
- Independently certified injection-attack detection (Ingenium Level 4, CEN/TS 18099 High)
- Science-based approach with a dedicated threat-intelligence operation
- Strong fit for high-assurance authentication and workforce security
3. Incode
Incode delivers a unified identity platform for high-volume onboarding, with deepfake defense built directly into its verification flow rather than added separately. Its Deepsight system is a purpose-built defense against AI-generated identities and manipulated media, combining biometric liveness, camera-injection analysis, and device signals to flag synthetic activity in real time.
Because Incode builds its technology in-house rather than assembling third-party components, it can retrain models quickly as new deepfake techniques appear, and its passive liveness keeps friction low for legitimate users at scale. A Gartner Magic Quadrant Leader used by major banks and global platforms, it is oriented toward organizations that want deepfake detection integrated into a full, high-throughput onboarding pipeline. Because Deepsight inspects the capture channel and device signals rather than only the final frame, it is positioned against injection attacks specifically, and its passive-liveness design means legitimate users are rarely asked to perform prompts, which keeps completion rates high even as the checks run in the background.
Key features:
- Deepsight deepfake defense integrated into the verification flow
- Camera-injection and virtual-camera analysis alongside device-integrity checks
- In-house models retrained continuously against emerging threats
- Passive liveness for low-friction, high-volume onboarding
4. Sumsub
Sumsub folds liveness and deepfake detection into a broader KYC, AML, and fraud platform, so synthetic-identity checks sit alongside document verification, screening, and ongoing monitoring in one configurable stack. Its deepfake defenses run inside the same flow that handles compliance, which appeals to teams that want a single platform rather than a standalone detector.
The configurability is the draw: operators can tune where and when deepfake and liveness checks fire, by market and risk level, and combine them with the platform’s wider fraud signals. For regulated businesses in crypto, fintech, and emerging markets that already lean on Sumsub for compliance, having deepfake detection in the same system reduces integration overhead. The platform also correlates deepfake and liveness signals with its wider fraud and behavioral analytics, so a suspicious selfie can be weighed alongside device, document, and transaction signals rather than judged in isolation, which helps reduce both missed synthetic identities and false rejections of genuine users.
Key features:
- Liveness and deepfake detection inside a full KYC, KYB, and AML platform
- Highly configurable checks by market and risk level
- Deepfake signals combined with broader platform fraud analytics
- SDKs, APIs, and hosted flows with bundled pricing
5. Reality Defender
Reality Defender is a deepfake-detection specialist rather than a full identity verification suite, focused on flagging AI-generated and manipulated media across image, video, audio, and text. It is often deployed where detection needs to run independently of, or as a layer on top of, an existing verification flow.
Its specialization is the appeal: for organizations that want a dedicated, continuously updated deepfake engine, whether to screen media, protect executives from voice deepfakes, or add a detection layer to another vendor’s onboarding, Reality Defender offers depth on the detection problem itself. Because it is a detection add-on rather than an end-to-end IDV platform, it is typically paired with a verification provider that handles documents, matching, and compliance. Its multimodal focus, spanning audio and video as well as images, also makes it relevant beyond onboarding, for example screening inbound video calls or voice interactions where deepfake impersonation of an executive or customer is the concern rather than a fabricated onboarding document.
Key features:
- Multimodal deepfake detection across image, video, audio, and text
- Specialist engine deployable as a standalone or add-on layer
- Continuously updated models focused solely on synthetic-media detection
- Useful for executive protection and media screening beyond onboarding
6. Socure
Socure is known for machine-learning-driven identity verification and fraud models, with particular strength in the US market and financial services. Its approach to synthetic identity leans heavily on data and predictive risk scoring, correlating identity signals to spot fabricated identities that do not behave like real people over time.
That data-centric angle complements image-based deepfake detection: where forensic tools inspect the document and face, Socure’s models assess whether the underlying identity is coherent across many signals, a strong defense against synthetic identities built to pass a single visual check. For US-centric financial institutions, its predictive fraud scoring is a well-established layer against synthetic fraud. Because synthetic identities are often built to look flawless on a single document check yet lack the deep history of a real person, Socure’s correlation of identity elements across many data sources can flag the inconsistencies that image-only inspection misses, making it a useful complement to forensic and biometric deepfake detection rather than a replacement for it.
Key features:
- Machine-learning fraud and identity-risk models
- Data-driven synthetic-identity detection across correlated signals
- Strong performance in US financial services
- High auto-approval rates for legitimate applicants
7. Onfido (Entrust)
Onfido, now part of Entrust, provides AI-powered document and biometric verification at enterprise scale, with deepfake and liveness detection embedded in its onboarding flow and backed by a wider identity and security portfolio. Its Atlas AI analyzes documents and biometrics for signs of manipulation and spoofing during verification.
The Entrust backing gives it a broad platform context, letting enterprises embed deepfake-aware verification into custom journeys through configurable orchestration. For organizations that want document and biometric deepfake checks within an established enterprise identity ecosystem rather than a point solution, Onfido is a common enterprise choice. Its Atlas AI is trained on a large and continuously growing set of verification data, which helps it generalize to new document types and manipulation techniques, and the Entrust portfolio lets enterprises extend from onboarding verification into ongoing authentication and credentialing under one vendor relationship.
Key features:
- Atlas AI document and biometric analysis with liveness and deepfake checks
- Configurable orchestration for custom onboarding journeys
- Part of the broader Entrust identity and security portfolio
- Enterprise-scale document and biometric verification
8. Veriff
Veriff pairs broad global document coverage with liveness and deepfake detection in a fast, consumer-friendly flow. Its verification combines automation with fraud analysis to flag manipulated media and spoofing while keeping onboarding quick, a balance that suits high-volume consumer platforms.
Veriff’s strength is coverage and speed: wide document support across many countries plus liveness checks that catch common presentation and manipulation attempts without heavy user friction. For global consumer apps and marketplaces whose priority is fast onboarding with a solid deepfake and liveness layer, it is a widely adopted option, typically evaluated against how deep its injection-attack defenses run for higher-risk flows. Veriff backs its automation with fraud-pattern analysis drawn from a large volume of cross-customer verifications, which helps it recognize recurring manipulation techniques, and its emphasis on conversion means the deepfake checks are tuned to run without adding noticeable friction for genuine users.
Key features:
- Broad global document coverage with liveness and deepfake checks
- Fast, consumer-friendly verification flow
- Fraud analysis combined with automated decisioning
- Wide adoption across global consumer platforms
Coverage by Attack Layer
| Vendor | Presentation | Injection | Synthetic Identity | Cross-Platform Reuse |
|---|---|---|---|---|
| AU10TIX | Yes | Yes | Forensic | Serial Fraud Monitor |
| iProov | Yes | Certified | Via liveness | Limited |
| Incode | Yes | Yes, Deepsight | Yes | Within platform |
| Sumsub | Yes | Yes | Yes | Within platform |
| Reality Defender | Media-based | Media-based | Media-based | Detection layer |
| Socure | Via data | Limited | Data-driven | US network |
| Onfido (Entrust) | Yes | Yes | Yes | Within platform |
| Veriff | Yes | Developing | Yes | Within platform |
Why Injection Attacks Changed the Game
The single most important shift in this category is the move from presentation attacks to injection attacks. A presentation attack shows something fake to the camera, a photo, a screen, a mask, and modern liveness detection has largely defeated it. An injection attack skips the camera entirely, using a virtual-camera app or an SDK-level hook to feed a pre-recorded or real-time deepfake straight into the verification pipeline. Because the synthetic feed never passes through a physical lens, any defense that only analyzes the final image is blind to it.
That is why the reported 1,151% year-over-year jump in injection attacks matters so much: the attack vector that is growing fastest is precisely the one older liveness checks were not designed to see. The vendors that hold up in 2026 inspect the capture channel and device integrity, not just the pixels, and pair that with detection of AI-generated documents and, critically, the ability to recognize a synthetic identity that has already surfaced elsewhere. Stopping deepfake and synthetic identity fraud is now a question of how many of those layers a vendor covers, not whether it offers liveness at all.