Sign-up → Licence and vehicle documents → Selfie and liveness → Face match → Background check → Activation → Periodic selfie re-checks
The selfie recurs, so an attacker gets more than one chance.
Ride-hailing and mobility / NeuroVerify
Deepfake Detection API for Ride-Hailing and Bike-Taxi Drivers
Ride-hailing and bike-taxi platforms depend on a selfie to prove the person driving is the person who was vetted. NeuroVerify checks driver selfies for deepfakes and manipulation beyond liveness and returns one unified risk score with signal-level evidence.
It runs behind the selfie step you already use at onboarding and at periodic or per-shift checks: no new step for the driver, one API call.
NeuroVerify does not verify licences, vehicle documents or background checks and does not decide who may drive; it adds authenticity evidence for your 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
A genuine licence does not prove the person at the wheel is the person on file.
Mobility platforms vet drivers with documents: a licence, vehicle registration, insurance and often a background check. Those documents prove a person and a vehicle exist. The selfie is meant to tie the driver to the file, both at onboarding and again before or during shifts.
Why identity is a safety issue here
A rider gets into a car on the strength of a name, a photo and a rating. If the driver is not the vetted person, the safety promise of the platform fails. Account sharing, rented accounts and fleet-operator models all stress that link.
Existing checks do not test authenticity. A face match shows two images look alike. Liveness shows a prompt was satisfied. Neither shows that the face is a live, unaltered capture from the device camera.
Background checks and police verification take days. The selfie takes seconds, so the quick tier tends to carry most of the identity risk until the slower checks finish.
Threats
How a vetted account ends up with the wrong face.
These are techniques, not claims about any platform. Each separates the vetted identity from the person driving.
01
Account sharing and fleet-operator models
One verified driver account is used by several people, with prepared or swapped faces used at periodic selfie checks.
02
Substitute face at the selfie step
Someone else holds a genuine licence and vehicle file and uses a synthetic or swapped face to pass the check.
03
Selfie built from the document photo
A face is generated or swapped from the licence photo so face match passes without a live person.
04
Photo-of-photo and screen replay
A printed photo or second screen is held to the camera to pass basic liveness.
05
Bulk onboarding through agents
An agent submits many drivers in a session, reusing or lightly editing media.
06
Reactivation across platforms and cities
A banned driver returns with a new account, a new name and a different face for the same person.
Recurring checks help only if each one tests whether the face is authentic.
Where NeuroVerify fits
Authenticity evidence at onboarding and at every re-check.
NeuroVerify sits behind the selfie capture in your driver onboarding and periodic verification. You send the image or video you already collect. It returns one unified risk score with evidence from six signals, and your policy decides whether to activate, ask for a fresh capture or review.
Using the same API at both onboarding and re-check lets you compare evidence over time. It checks face and video evidence, not licences or vehicles.
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
Send the driver selfie at onboarding
Pass the image or video from your existing capture step to the API.
Read the unified risk score
Get one score with signal-level evidence in a single response.
Apply your safety policy
Activate, request a new capture, route to a human reviewer or decline, by city and vehicle type.
Repeat at periodic and per-shift checks
Send re-check selfies to the same API and keep the evidence with the driver record.
What you get
What mobility platforms gain.
Stronger driver identity assurance
Reduce the chance that a vetted account is used by someone else.
Fewer shared and rented accounts
Catch substituted faces at onboarding and at re-checks.
Safety evidence for incidents
Keep the authenticity record with the driver file for investigations.
No new friction for drivers
They complete the selfie step they already know.
Mobile-first integration
Android, iOS and Flutter SDKs plus REST and backend SDKs.
Questions
What teams ask before they integrate.
How does NeuroVerify fit into your driver onboarding and re-verification flow?
It sits behind the selfie or face-capture step you already run. You send the image, video or audio you already collect to one API call, and the response comes back with a unified risk score and signal-level evidence. The end user sees no new step.
Integration uses native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) or the REST API. Two lines to integrate and live in under 24 hours are product claims; your own environment and review process set the real timeline.
We already run liveness. Why add deepfake detection?
Liveness asks whether a live person appears to be in front of the camera. It was designed around printed photos, screen replays and masks. It does not ask how the media was produced, so a face swap, a fully synthetic face or an injected stream can satisfy it.
NeuroVerify checks for deepfakes and manipulation beyond liveness. Keep your liveness vendor and add the authenticity evidence next to it. See deepfake detection vs liveness detection.
What does NeuroVerify return, and who makes the decision?
One unified risk score, plus evidence from six signals: Visual Safety Screening, Live Presence Verification, Face Manipulation Detection, AI Generative Model Analysis, AI Pixel-Level Manipulation and AI Content Compliance.
Your policy decides what happens next: approve, step up, route to manual review or decline. NeuroVerify does not approve or reject the driver or the account.
How fast is it, and how is it benchmarked?
Latency is under 200 ms p95 on eligible image requests under defined test conditions. The methodology is available on request, so you can compare it with your own traffic.
On the public VendorBench-100 benchmark, NeuroVerify ranked first of 36 systems, with a detection score (MCC) of 0.876 against 0.646 for the next-best vendor.
Where does the media go, and what deployment options exist?
Deployment options are a managed cloud API, private cloud or customer VPC, a secure container, on-premises, and air-gapped. Pick the one that matches your data residency and security requirements.
The platform holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II certifications. For handling details, see product data handling and the trust centre.
Can we test it on our own media before committing?
Yes. Send a representative sample of driver selfies from onboarding and from periodic checks, across cities and device types and compare the scores with your own labels. Include genuine captures from low-end handsets and poor lighting so you can see how thresholds behave on real users, not just on attacks.
Contact the team to set up an evaluation. See also how to evaluate a detection API.
Can we use it for per-shift or random re-verification?
Yes. Any face image or video you send gets the same checks. Teams typically compare the new capture with earlier evidence and run a face-match step separately. Frequency and triggers are your policy decision.
Evaluate NeuroVerify
Test driver selfies from onboarding to re-check.
Send NeuroVerify a sample of driver selfies from onboarding and re-verification and compare the scores with your existing checks.