Register → Upload documents → Selfie and liveness → Background check → Account live
The selfie matches the documents. Nothing checks whether it is a real, unmanipulated face.
Gig and delivery platforms / NeuroVerify
Deepfake Detection API for Delivery-Rider Onboarding
Delivery and gig platforms onboard riders and drivers at volume, with a selfie and a document check. NeuroVerify checks that selfie for deepfakes and face manipulation beyond liveness, and returns one risk score with signal-level evidence before the worker goes live.
It runs on the selfie your app already captures: no new step for the rider, one API call for you.
NeuroVerify does not verify licences, run background checks or decide who can work. Your onboarding vendors and platform policy keep those jobs.
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
Riders can be onboarded under someone else's face.
Gig platforms need to grow their workforce quickly. Onboarding usually asks for identity and licence documents, bank details, a background check and a selfie. The selfie is meant to show that the person registering is the person on the documents, and later that the person riding is the person approved.
Two kinds of fraud sit behind this. Some applicants have no right to work or have been barred, and use a stolen or purchased identity. Others sell or rent approved accounts to third parties. In both, the selfie can be a synthetic or manipulated face that matches the documents on file.
Where the risk shows up
Account sharing and identity substitution lead to safety issues, insurance disputes, payment fraud and platform liability. Periodic re-verification selfies face the same weakness. See identity review for gig worker onboarding and synthetic employee hiring fraud.
Threats
Attacks on gig and rider onboarding
These are attack patterns, not claims about any specific platform.
01
Barred or ineligible worker re-enrolling
A person removed from the platform returns with a manipulated face to evade a match with their earlier record.
02
Rented or sold rider accounts
An approved account is handed to someone else, and a fake or swapped face is used at re-verification.
03
Stolen identity with a face-swapped selfie
The attacker renders the owner of stolen documents onto their own face to pass match.
04
Fully synthetic worker identities
A fabricated person with a generated face collects payouts or incentive bonuses.
05
Virtual camera in the app
A pre-recorded or generated clip replaces the live camera feed during capture.
06
Referral and bonus abuse at scale
One operator creates many rider accounts from a small set of faces to farm sign-up rewards.
Related: synthetic identity fraud and synthetic personas and fake profiles.
Where NeuroVerify fits
Authenticity evidence before a worker goes live
NeuroVerify sits right after selfie capture in your rider app and before your policy activates the account. Document checks, background screening and payout setup stay as they are.
Upload documents → Selfie → NeuroVerify risk score → Background check → Account live or held
One added call. The rider still takes one selfie.
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. NeuroVerify analyses face images, video and audio, not documents.
Android/Kotlin, iOS/Swift and Flutter SDKs fit rider apps built for low-end devices and variable networks. See digital onboarding and NeuroVerify.
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 rider selfie
Pass the image your app already captures during onboarding or re-verification.
Read the unified risk score
Get one score with signal-level evidence in the response.
Activate, review or decline
Apply your own thresholds for activation, manual review or rejection.
Keep evidence with the rider record
Store the result for later disputes, safety reviews and re-verification comparisons.
What you get
What it means for a gig platform
Fewer fake and rented accounts
Manipulated faces are flagged before the account goes live.
Safer platform for customers
The person approved is more likely to be the person riding.
Protects bonus and referral budgets
Mass-created accounts from reused faces are easier to catch.
Works with varied devices
SDKs and REST suit the phones and networks your riders actually use.
Clear evidence for disputes
Signal-level results support decisions on suspended or rejected riders.
Questions
What teams ask before they integrate.
Can we use it for periodic re-verification as well as first onboarding?
Yes. Any step where your app captures a selfie can send it to NeuroVerify. Re-verification is a common place to catch account sharing, because attackers often swap the face after approval.
Will it work on low-end phones and poor connections?
NeuroVerify analyses the media you send, so image quality and compression matter. Test it on samples from the devices and networks your riders use. Native mobile SDKs help control capture quality in the app.
Do we still need a separate liveness check?
Not for this purpose. Live Presence Verification is one of the six signals NeuroVerify runs in a single request, alongside Face Manipulation Detection, AI Generative Model Analysis, AI Pixel-Level Manipulation, Visual Safety Screening and AI Content Compliance. It checks selfie images and video from rider onboarding for deepfakes and manipulation beyond liveness.
If you already run a liveness vendor, keep it during evaluation and compare outcomes on your own media before deciding what to retire. The difference is explained in Deepfake Detection vs Liveness Detection.
How much work is the integration?
It fits the capture step you already run, so the end user sees nothing new. You send the media you already collect to one API call. Native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) and a REST API are available, and an MCP server covers AI agents.
Two lines to integrate and live in under 24 hours are product claims. Your own timeline depends on how your rider app is wired.
How fast is it, and will it slow the journey?
NeuroVerify returns in under 200 ms at p95 on eligible image requests under defined test conditions. The methodology is available on request. Video and audio take longer than a single image, so decide with your team which step runs inline and which runs in the background.
Where does the data go, and how can we deploy it?
You choose the deployment model: managed cloud API, private cloud or customer VPC, secure container, on-premises, or air-gapped. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II certifications. Your legal team decides consent, retention and residency for the media you send.
Details are on the deployment and product data handling pages. For the privacy angle, see GDPR, Biometrics, and Authenticity Analysis.
Can we evaluate it on our own media?
Yes, and that is the test that matters. 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. A benchmark does not reflect your devices, lighting, compression or attack mix, so bring representative samples from your own journey.
Contact us to set up an evaluation.
How do we handle false positives and set thresholds?
NeuroVerify returns one unified risk score with signal-level evidence. It does not approve or reject anyone. Your policy sets the thresholds and decides what happens at each level: continue, step up to a manual review, or decline.
The signal-level evidence tells a reviewer why a case scored high, which makes review queues faster and decisions easier to explain later.
Evaluate NeuroVerify
Try it on your own rider selfies.
Send samples from your onboarding and re-verification flows and review the risk scores and evidence. Contact us to plan an evaluation.