Client app → Provider SDK → Document check → Liveness → Face match → Result to client
The client receives a pass or fail. If a deepfake passed liveness, there is no authenticity signal for the client to act on.
eKYC providers and platforms / NeuroVerify
Deepfake Detection API for eKYC API Providers
If you sell identity verification to banks, fintechs, insurers or platforms, your clients ask whether your selfie step catches deepfakes. NeuroVerify lets you answer with one API call that checks selfie, video or audio beyond liveness and returns one unified risk score with signal-level evidence.
It fits inside your existing capture and liveness pipeline: send the media you already receive, with no new step for your clients' end users.
NeuroVerify does not replace your document, liveness or matching stack. It adds authenticity evidence you can pass through to clients, who set their own policy.
Powered byNeuroVerifyExplore the product
- #1 of 36on the public VendorBench-100
- <200 msp95 on eligible image requests
- 6 → 1six checks, one unified score
- 0new steps for your customer
The gap
Clients now expect deepfake coverage, not only liveness.
Providers compete on pass rates, speed and coverage of countries and document types. Liveness has long been the answer to spoofing. Clients' fraud teams now ask a different question: can the product detect face swaps, generated faces and injected video?
Building that capability in-house means collecting attack data, training detectors and keeping up with new generation tools. Partnering with a specialist can be faster, but it must not add latency, a new user step or a vendor management burden for your clients.
Where the gap shows up
Multi-tenant platforms also serve clients with different risk appetites. They need a score and evidence, not a single yes or no. See Why Neural Defend for KYC and Deepfake Detection for Identity Teams.
Threats
What your clients are worried about.
These are attack techniques, not claims about any specific provider or client. Each is a question your clients may ask you.
01
Face swap that follows liveness prompts
A live swap responds to challenges, so prompt-based liveness can pass. Clients want to know if you catch it. See Face Swap Detection for KYC.
02
Virtual camera and injected video
Prepared video replaces the camera feed ahead of your SDK's checks. See Virtual Camera Injection Attacks.
03
Synthetic faces at scale
Generated faces paired with fabricated identities arrive through many clients at once, so one client alone cannot see the pattern.
04
Selfies engineered to match documents
Edited or generated selfies resemble a stolen document's portrait, passing face match. See Synthetic Document and Selfie Fraud.
05
Audio deepfakes in voice and video KYC
Cloned voices appear in assisted or video verification calls offered by your clients. See Voice Deepfake Detection for KYC.
06
Replay of genuine sessions
Earlier genuine captures are replayed into new attempts through the same provider endpoints.
Clients in regulated sectors will ask about certifications and data handling. NeuroVerify shows ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II. See Trust.
Authenticity evidence supports your clients' controls. Each client's policy decides outcomes.
Where NeuroVerify fits
Add deepfake detection to your product without rebuilding it.
NeuroVerify sits inside your pipeline after selfie capture and before the result is returned to your client. You can expose the unified risk score and evidence as a new field, or use it inside your own decisioning.
Client app → Capture → Liveness → Face match → Pass or fail
Clients get no separate signal on whether the face is real.
Client app → Capture → NeuroVerify risk score → Liveness and face match → Result plus authenticity evidence
One call added. Clients see more, and their users see nothing new.
What runs in the request
Six signals run in one request: Visual Safety Screening, Live Presence Verification, Face Manipulation Detection, AI Generative Model Analysis, AI Pixel-Level Manipulation and AI Content Compliance. You get one unified risk score with signal-level evidence. See NeuroVerify and Deployment.
What stays with you
Your product, SDK, pricing, client relationships and decision logic stay with you. NeuroVerify supplies authenticity evidence you can surface or use internally.
NeuroVerify Unified Trust LayerSix parallel analysis layers
- Visual Safety Screening
- Live Presence Verification
- Face Manipulation Detection
- AI Generative Model Analysis
- AI Pixel-Level Manipulation
- AI Content Compliance
Add NeuroVerify after selfie capture
Call it from your backend with a Python, TypeScript/Node.js, Go or Java SDK or the REST API, on media you already receive.
Read the unified score
Get one risk score with signal-level evidence, typically under 200 ms p95 for eligible image requests.
Expose it to clients
Return the score and evidence as a response field, with thresholds clients can configure per tenant.
Choose a deployment
Use the managed cloud API, or a private cloud, container, on-premises or air-gapped option for clients with strict residency rules.
What you get
What identity providers get.
A deepfake answer for clients
Respond to RFPs and security reviews with detection beyond liveness, backed by a public benchmark: first of 36 systems on VendorBench-100.
No rebuild of your stack
Add one call to your pipeline instead of building and maintaining detection models yourself.
Per-tenant policy
Because you get a score and evidence, each client can set its own thresholds.
No added user friction
Your clients' users take the selfie they already take.
Fits your hosting model
Cloud, VPC, container, on-premises and air-gapped options match different client requirements.
Developer-ready
Mobile SDKs for Android, iOS and Flutter, backend SDKs, a REST API and an MCP server. Two lines to integrate.
Questions
What teams ask before they integrate.
Can we embed NeuroVerify in our own product?
Yes, providers can call NeuroVerify from their pipeline and surface the result to their clients. Talk to us about commercial terms, branding and multi-tenant setup.
Will it slow our verification flow?
NeuroVerify targets under 200 ms p95 on eligible image requests under defined test conditions. You can run it in parallel with other checks so it does not extend the overall journey. Methodology is available on request.
We already run liveness. Why add NeuroVerify?
Liveness answers whether a live person appears to be present. It was not built to answer whether the face, video or audio was generated or manipulated. A face swap, a virtual camera feed or a synthetic video can still pass a liveness prompt.
NeuroVerify includes Live Presence Verification as one of six signals, then adds face manipulation, generative-model and pixel-level analysis in the same request. You keep your existing liveness step and add authenticity evidence to it. See Deepfake Detection vs Liveness Detection.
How hard is it to integrate?
Most teams send the media they already capture. There is no new step for the end user. NeuroVerify offers native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java), a REST API and an MCP server for AI agents.
Integration is two lines of code, and teams can be live in under 24 hours. That is a product claim; your timeline depends on your own change process.
Where can it be deployed, and how is data handled?
NeuroVerify can run as a managed cloud API, in a private cloud or customer VPC, as a secure container, on-premises, or air-gapped. Pick the option that fits your security review and data-residency rules. See Deployment and Product data handling.
Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II certifications, shown on the Trust page.
Can we test it on our own media first?
Yes. We recommend evaluating on your own captures, including real customer media and the attack types you worry about most. Use your own acceptance criteria rather than a vendor demo set.
For context, NeuroVerify ranked first of 36 systems on VendorBench-100, with a detection score (MCC) of 0.876 against 0.646 for the next-best vendor. Read How to Evaluate Deepfake Detection Vendors for a checklist.
Who makes the final decision, and what about false positives?
You do. NeuroVerify returns one unified risk score with signal-level evidence. Your own policy decides what to approve, send to review or block. No detector is perfect, so set thresholds against your own risk appetite and review volume.
Because the evidence is signal-level, reviewers can see why a case was flagged instead of facing an unexplained score.
Evaluate NeuroVerify
Give your clients deepfake detection beyond liveness.
Send us media from your capture pipeline and compare the unified risk score and evidence with what your clients see today.