Government and public services / NeuroVerify
For agencies and programme operators that enrol citizens, pay benefits and confirm that pensioners are alive, remotely. NeuroVerify checks the selfie or video for deepfakes and manipulation beyond liveness, so benefits reach the person entitled to them.
It fits the selfie or video step your enrolment already runs: one API call, no new step for the citizen, one unified risk score with signal-level evidence.
NeuroVerify does not verify citizen records, decide eligibility or issue credentials. It adds authenticity evidence for face, video and audio; programme rules and caseworkers decide.
The gap
Governments are moving enrolment, benefit claims, licensing and proof-of-life online. Citizens use phones and shared devices, and many do so through assisted channels or intermediaries. That access is the goal, and it needs to be inclusive.
Public programmes pay out real money and issue credentials with legal weight. A benefit, pension, licence or digital ID obtained under a false identity harms the genuine citizen and the programme. Fraud that gets past the face check also tends to repeat across programmes.
Registry lookups confirm that a record exists. Face match confirms two images look alike. Liveness confirms a prompt was followed. None of them tells a caseworker whether a face was generated, swapped or injected. See deepfake risk controls for public benefits and synthetic media controls for government enrollment.
Citizen logs in → Claim or life-certificate request → Selfie or short video → Liveness and face match → Caseworker or system approval → Payment or credential issued
The selfie or video is the key evidence in the file. NeuroVerify adds an authenticity read to it.
Remote proof-of-life for pensions is one example. Several countries run digital life-certificate schemes, and the same risk applies to any remote check of a person.
Threats
These are attack techniques, not claims about any agency or country.
An attacker uses a stolen identity and a swapped or generated face at enrolment or claim.
A synthetic video or a stand-in is used to keep a pension or benefit payment flowing.
An assisting agent submits many enrolments with substituted faces or injected video.
A fabricated person with a generated face is enrolled to obtain a credential that unlocks other services.
A swapped face is used to renew or transfer licences and permits remotely.
A cloned voice or video impersonates a recipient or official to redirect payments.
Public sector use of biometrics carries extra privacy and fairness duties. See GDPR, biometrics and authenticity analysis and EU AI Act Article 50 and synthetic content for context. Your own legal team sets what is permitted.
For wider guidance, see deepfake detection for government and public sector and the government industry page.
Where NeuroVerify fits
NeuroVerify sits at the selfie or video step of enrolment, claim and proof-of-life, before a payment or credential is issued. Your portal or app sends the media it already collects. NeuroVerify runs six signals in one request and returns a unified risk score with signal-level evidence.
Programme policy decides next steps: approve, ask for another channel, send to a caseworker, or require in-person verification. Inclusion matters, so a flag should lead to an alternative route, not a dead end.
Pass the media from your portal, app or assisted-service kiosk to NeuroVerify with one API call.
Get one score with evidence from the six signals.
Approve, step up through another channel, route to a caseworker or require in-person verification.
Store the score and signal evidence with the claim so audits and appeals have context.
What you get
Catch synthetic or swapped faces before money or a credential is issued.
Citizens use the same selfie or video. Flags route to alternatives, not to refusal.
Signal-level evidence helps caseworkers prioritise cases instead of reviewing everything.
Run in a private cloud, on-premises or air-gapped to meet data-residency needs.
Evidence from each signal supports audit and appeals.
Questions
No. NeuroVerify fits the selfie or video step your journey already runs. You send the media you already capture in one API call. There is no new step for the end user.
Integration uses native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) or REST. Two lines of code is the product claim; live in under 24 hours is the target.
Not as a replacement. Liveness asks whether a live person was in front of the camera. NeuroVerify asks whether the media itself is authentic, and it includes a Live Presence Verification signal alongside five others, so it checks beyond liveness. See deepfake detection vs liveness detection.
You can keep your current liveness vendor and send the same selfie or video to NeuroVerify as well.
Yes. Remote proof-of-life asks a recipient to show a selfie or short video. NeuroVerify checks that media for deepfakes and manipulation, which a prompt-based liveness check alone does not.
No. NeuroVerify checks face images, video and audio. It does not check registries, documents or eligibility rules. Those remain with your programme systems.
Yes. NeuroVerify can run in a private cloud or customer VPC, as a secure container, on-premises or air-gapped. See data residency for deepfake detection.
Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II.
NeuroVerify returns in under 200 ms p95 on eligible image requests under defined test conditions. The test methodology is available on request, so you can compare it with your own traffic and network path.
Image checks run inside the capture step, so the end user does not see a separate wait. Video and audio take longer because they carry more media; ask us for numbers that match your clip lengths.
NeuroVerify returns one unified risk score plus signal-level evidence, so a reviewer can see which of the six signals raised concern. It does not make the final decision. Your own policy decides whether to approve, step up, route to manual review or decline.
Because thresholds are yours, you can tune them to your own tolerance for false positives and false negatives. See deepfake detection errors.
Yes. We recommend evaluating on your own media: your real capture conditions, devices and attack samples. Start with a small test set, compare the unified score and signal evidence against your current checks, then set thresholds with your own policy team.
NeuroVerify ranked #1 of 36 systems on VendorBench-100 (detection score MCC 0.876 versus 0.646 for the next-best vendor), but a public benchmark is a starting point. Your own media is the real test. Contact us to set one up.
Evaluate NeuroVerify
Send representative selfies and video from enrolment, claims and proof-of-life. We will show the unified score and evidence so your programme can judge fit in your own environment.
