Sign-up → Licence and vehicle documents → Selfie and liveness → Face match → Background and compliance checks → Activated partner
Every step can pass while someone else does the work behind the approved account.
Logistics and transportation / NeuroVerify
Deepfake Detection API for Logistics and Transportation
Freight, delivery, ride and fleet platforms onboard drivers and partners remotely, often at high volume. NeuroVerify checks the onboarding selfie and re-verification video for deepfakes, swaps and replays, and returns one risk score with evidence.
It fits the selfie or liveness step your partner app already runs: no new step for the driver, one API call on the media you collect.
NeuroVerify does not verify licences or vehicle papers and does not decide who may work on your platform. It adds authenticity evidence; your safety and compliance 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
Every document can check out while the wrong person is cleared.
Logistics and mobility platforms verify driver licences, vehicle registrations and background records, then confirm the person with a selfie. The selfie is the link between a clean paper file and the human who will carry goods or passengers.
That link is weak. A genuine licence with a manipulated or substituted selfie produces a fully consistent file for the wrong person. Basic liveness may catch a printed photo, but it may not catch a generated face or a swapped video stream.
Why the problem repeats after onboarding
Accounts get shared, sold or handed to unverified drivers. Platforms that re-verify with a selfie at shift start or on a trigger face the same issue: a replayed photo or video can stand in for the live driver. Authenticity checks at each of those points protect delivery safety, insurance cover and customer trust.
Threats
How the wrong person gets cleared.
These are attack techniques seen in remote workforce and partner verification, not claims about any specific company.
01
Approved account, unverified driver
A verified partner lets someone else drive under their identity. A recorded or replayed selfie passes shift-start checks while the real driver is elsewhere.
02
Selfie substitution at onboarding
The applicant submits a generated or face-swapped selfie built from the face on a borrowed licence, so the face match looks perfect.
03
Photo-of-photo and screen replay
A photo or video shown on a second screen is captured in the app. Simple checks may accept it as a live face.
04
Ghost fleet partners
Entire driver or vehicle entries are created with synthetic faces so payouts and incentives flow to accounts that do not exist.
05
Agent-assisted bulk onboarding
A recruiter completes selfie steps for many applicants, or feeds manipulated media, to hit onboarding targets.
06
Identity recycled across networks
A banned or rejected person returns on another platform or region with a swapped face. See synthetic identity fraud.
Background: replay attacks in identity proofing and virtual camera injection attacks.
Related workforce guidance: Deepfake Controls for Remote Hiring.
Where NeuroVerify fits
Authenticity checks at onboarding and re-verification.
NeuroVerify sits behind the selfie or video capture in your partner app. Use it at sign-up and at any re-verification point, such as shift start, payout change or account reactivation.
Sign-up → Licence and vehicle documents → Selfie or video capture → NeuroVerify → Face match → Risk score and platform policy
The same call works at re-verification. Your background and compliance vendors stay as they are.
What runs in one request
NeuroVerify checks the face image, video or audio for deepfakes and manipulation beyond liveness. All six signals run together and resolve into one risk score:
- Visual Safety Screening - Live Presence Verification - Face Manipulation Detection - AI Generative Model Analysis - AI Pixel-Level Manipulation - AI Content Compliance
The response carries the unified score plus signal-level evidence. Your own policy makes the final decision.
What stays with you
Your licence and background-check vendors, your safety and insurance rules, your payout controls, your recruiter and agency oversight, your consent and retention rules, and the decision on every partner.
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 partner selfie or video
Pass the image or frames the driver app already captures at sign-up or re-verification.
Call one API
Use the REST API or a mobile or backend SDK from your partner platform.
Read the unified risk score
Get one score with signal-level evidence for the partner record.
Route by your policy
Activate, ask for a retry in person or at a hub, or block, under your own rules.
What you get
What logistics and mobility teams get.
A safer driver base
Impersonators and shared accounts are harder to slip through onboarding and shift checks.
Less payout and incentive leakage
Ghost partners and recycled identities are flagged before payouts begin.
Scalable at high volume
One API call per check fits high-throughput onboarding without a new user step.
Consistent checks across regions
The same call covers every market and channel, including agent-led onboarding.
Evidence for disputes and audits
Signal-level output supports safety, insurance and compliance reviews.
Questions
What teams ask before they integrate.
We already run liveness. Do we still need NeuroVerify?
Liveness answers whether a live person appears to be in front of the camera. It was built for presentation attacks such as a printed photo, a mask or a screen held to the lens. It was not built to tell whether the stream reaching your server came from a camera at all, or whether the face inside it was generated or swapped.
NeuroVerify runs inside the selfie or video step you already have. Live Presence Verification is one of its six signals, and the other five look for face manipulation, generative-model traces, pixel-level edits and unsafe content in the same request. At scale, that means one check for drivers on low-end phones and mixed networks. You can keep your current liveness vendor during an evaluation and compare outcomes on your own media. See deepfake detection vs liveness detection.
Does it verify driving licences or vehicle papers?
No. NeuroVerify analyses face images, video and audio only. Licence and vehicle checks stay with your document and background vendors.
Can we use it for re-verification, not just onboarding?
Yes. The same call can run on a selfie at shift start, account reactivation or a payout change, so a recorded or swapped face is not enough to take over an approved account.
How hard is it to integrate?
NeuroVerify offers native mobile SDKs (Android/Kotlin, iOS/Swift, Flutter), backend SDKs (Python, TypeScript/Node.js, Go, Java) and a REST API. Integration is two lines of code, and teams can be live in under 24 hours.
Native Android and iOS SDKs and a Flutter SDK suit driver apps, and backend SDKs support partner portals.
How is our data handled, and where can it run?
NeuroVerify analyses the face images, video or audio you send and returns a risk score with evidence. Consent, retention and residency stay under your policy, which matters when drivers sit in many jurisdictions. See GDPR, biometrics and authenticity analysis for one framework. Consent, retention and residency stay under your policy. See product data handling for how media is treated.
Deployment options include the managed cloud API, private cloud or customer VPC, a secure container, on-premises and air-gapped environments. Neural Defend holds ISO/IEC 27001, ISO/IEC 27701, ISO/IEC 42001 and SOC 2 Type II certifications, shown on the trust page.
How fast is it, and will it slow the journey?
NeuroVerify targets under 200 ms at p95 on eligible image requests under defined test conditions. The test methodology is available on request, so you can check it against your own media sizes and network path.
It is one API call on media you already capture, so it adds a single request rather than a new user step. Driver apps often run on weak networks, so test on realistic connections.
How do we handle false positives and set thresholds?
NeuroVerify returns one unified risk score and signal-level evidence. It does not approve or reject anyone. Your policy sets the thresholds and decides what each band does.
A common pattern is to send mid-band results to a retry or an in-person check rather than rejecting a driver outright.
Can we test it on our own media first?
Yes, and that is the test that matters. Public results help: NeuroVerify ranked first of 36 systems on VendorBench-100, with a detection score of MCC 0.876 against 0.646 for the next-best vendor. But your capture devices, compression and attack mix decide real performance.
Use authorized onboarding and shift-start samples from low-end devices and poor lighting. Compare scores and evidence against your current decisions. For a vendor shortlist, see the top 5 deepfake detection APIs.
Evaluate NeuroVerify
Test NeuroVerify on your driver and partner media.
Bring authorized media from your own journey. NeuroVerify returns a risk score with signal-level evidence, so you can compare it with your current checks before changing anything.