Sign-up → Document upload → Selfie and liveness → Face match → Approved to work → Periodic selfie re-check
Each selfie moment can be passed with synthetic or swapped media. NeuroVerify adds an authenticity read at those moments.
Gig economy / NeuroVerify
Deepfake Detection API for Gig Economy Platforms
For ride-hailing, delivery, freelance and home-services platforms that onboard thousands of workers. NeuroVerify checks the onboarding and re-verification selfie for deepfakes and manipulation beyond liveness, so the verified worker is the one doing the job.
It fits the selfie step your onboarding already runs: one API call, no new step for the worker, one unified risk score with signal-level evidence.
NeuroVerify does not run background checks or verify licences and documents. It adds authenticity evidence for the face, video and audio; your trust and safety policy decides.
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
The verified worker and the working worker can be different people.
Gig platforms grow by making onboarding quick. A worker uploads details and a selfie, passes a check and starts earning. Trust and safety then depends on the idea that the person on the job is the person who was verified.
That assumption is attacked from two sides. Banned or unqualified workers want back in under a new identity. Others rent or sell verified accounts to someone else. Both need to pass a selfie step that was designed to prove presence, not authenticity.
Why selfie re-checks are not enough alone
Many platforms re-verify at login or before a job. That helps, but liveness and face match can be fooled by a face swap, a replay or injected video. The cost lands on customers and on workers who followed the rules. See identity review for gig worker onboarding.
Threats
How deepfakes reach gig platforms
These are attack techniques, not claims about any platform.
01
Banned workers returning with new faces
A previously removed worker re-registers using a generated or swapped face on a fresh identity.
02
Rented or sold verified accounts
A verified worker lets another person work the account, and a swap at re-check hides the change.
03
Synthetic identity farms
Rings register batches of accounts with generated faces to harvest sign-up incentives or receive payouts.
04
Camera injection during selfie checks
Emulators and virtual cameras feed prepared video so no real face is ever captured.
05
Fake customer or driver impersonation
A swapped face is used to appear as a legitimate driver or courier to a customer or support agent.
06
Payout and account takeover
A deepfake of the real worker passes a selfie re-check, letting an attacker change payout details.
Gig platforms operate in many markets with different worker rules. The authenticity risk is the same: the selfie is trusted because it was captured live.
See also seller identity controls for marketplace onboarding and deepfake detection for online marketplaces.
Where NeuroVerify fits
Where NeuroVerify sits in worker onboarding
NeuroVerify sits at each selfie moment: onboarding, periodic re-check and payout change. Your app sends the selfie or video it already captures. NeuroVerify runs six signals in one request and returns a unified risk score with signal-level evidence.
Your trust and safety policy decides next steps. Your background-check and document vendors stay in place.
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 worker selfie
Post the onboarding or re-check selfie from your worker app to NeuroVerify with the native SDK.
Read the unified risk score
Get one score with evidence from the six signals.
Apply your trust and safety policy
Approve, ask for a supervised re-check, route to review or deactivate.
Re-use at risky events
Run the same check on payout change, account recovery and long inactivity.
What you get
What gig platforms gain
Keep banned workers out
Catch swapped or generated faces at re-registration.
Reduce account renting
Stronger re-checks make handing over a verified account harder.
No extra steps for workers
Workers take the same selfie. Genuine workers are unaffected.
Safer for customers
Customers see a worker who matches the verified identity.
Built for volume
Image checks return in under 200 ms p95 under defined test conditions.
Questions
What teams ask before they integrate.
Will this slow down worker sign-up?
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. Native Android, iOS and Flutter SDKs keep capture inside your app.
We already run selfie re-checks. Is that enough?
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.
Does it replace background checks or licence checks?
No. NeuroVerify checks face images, video and audio only. Background checks, licence and document verification stay with your current vendors.
Can it detect that two accounts share one person?
NeuroVerify scores the authenticity of each piece of media. Matching faces across accounts is a separate analytic that your own systems or vendors may run. The authenticity evidence can feed those rules.
Can it keep up with surge onboarding?
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.
Which deployment options are available?
NeuroVerify runs as a managed cloud API, in a private cloud or your own VPC, as a secure container, on-premises, or air-gapped. The right option depends on your data-residency and security rules.
The same six signals and the same unified risk score are available in each model. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II; see the Trust Center for details.
Who decides when a worker is deactivated?
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.
Can we test it on our own fraud cases?
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
Test NeuroVerify on your worker onboarding media
Send selfies from real onboarding and from known fraud cases. We will show how the unified score separates them before you change a flow.