HyperVerge Fraud Prevention
The face passed every check. It was generated — HyperVerge Fraud Prevention was the only system of sixteen to meet every DHS RIVTD Track 2 benchmark — deepfake detection, deduplication and AML on the same journey as onboarding.
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How it’s rated
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The HyperVerge platform family
This page covers Fraud Prevention — deepfake, forgery and AML. The rest of the platform:
Quick facts
30-second orientation- Product
- Fraud Prevention — deepfake, forgery, dedup, AML
- Inside it
- DeepfakeSafe, face dedup, forgery check, device intel, AML
- DHS RIVTD 2024
- Only 1 of 16 to meet all Track 2 benchmarks
- Liveness
- iBeta / ISO 30107-3 Level 2 certified
- Honest scope
- Certifications are point-in-time, not permanent
- The moving part
- Injection attacks bypass the camera entirely
- Pricing
- Quote-only — typically per check
- In India via
- TechBag — INR/GST, scoping and support
Understand identity fraud before you buy against it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is HyperVerge Fraud Prevention?
Deepfake detection, deduplication, forgery checks and AML on one journey — with the DHS RIVTD Track 2 sole pass as its strongest measured evidence.
A blink prompt vs measured detection — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | A blink prompt and a reviewer | Fraud Prevention (HyperVerge) |
|---|---|---|
| Liveness | A blink prompt | ISO 30107-3 Level 2 certified |
| Deepfakes | Not considered | DeepfakeSafe at the point of capture |
| Injection | Invisible | Device intelligence — ask how it is handled |
| Repeat fraud | Each application judged alone | Dedup across everyone enrolled |
| AML | A separate batch queue | Screened inside the same journey |
| What it is NOT | — | Not a permanent guarantee — attacks move |
Ask about INJECTION attacks separately from liveness: ISO 30107-3 certifies presentation attacks, and a virtual camera never presents anything. Certifications are point-in-time — ask about retraining cadence too.
The four pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Presentation attack detection
Tells a live person from a printed photo, screen replay, mask or rendered deepfake, certified against ISO 30107-3 Level 2 on a defined set of attack instruments.
Injection attack defence
A virtual camera feeding synthetic video into the app never passes a lens, so presentation-attack detection alone misses it. Ask how this fast-growing attack is handled.
Face deduplication
Matches a new applicant’s face against everyone already enrolled, catching one person opening accounts under different identities that per-application checks pass.
Document forgery and AML
Detects tampering on submitted documents — edits and template mismatches — and screens against sanctions and PEP watchlists.
One defence line from the camera to the watchlist — liveness, injection defence, face dedupe and forgery checks together.
Six capabilities. Detect, dedupe, screen.
HyperVerge Fraud Prevention catches the synthetic and the repeat — deepfakes, dedup and the rest of the HyperVerge portfolio.
Detect the generated face
Identifies synthetic and manipulated faces at verification — the capability behind the DHS Track 2 result.
Certified to ISO 30107-3 L2
Presentation attack detection tested by iBeta against photos, replays and masks, on a published protocol.
The same face, a second time
Matches against everyone already enrolled to catch one person opening several accounts, which per-application checks miss.
Find the edited document
Tampering and template-mismatch detection on submitted documents. A different question from whether the person is real, and one a face check cannot answer.
Signals the face cannot give
Spots emulators, virtual cameras and repeated device fingerprints, often catching injection attacks biometrics miss.
Sanctions and PEP lists
Watchlist screening runs in the same journey, so a hit surfaces before onboarding completes, not in a batch review.
Watch HyperVerge in action
How the NIST benchmarks work, and fraud prevention deployed in the field.
Acing NIST — the Olympics of Face Recognition
How face-recognition benchmarks actually work.
Flip x HyperVerge — Fraud Prevention in Fintech
Fraud prevention deployed in a fintech.
ClientsSpeak — WazirX
Verification at exchange scale.
Want a live, India-context walkthrough for your environment?
Book a guided demo →A reviewer trusts their eyes. The deepfake counts on it.
Here’s what genuinely sets it apart — and exactly where it stops.
The DHS Track 2 result, stated precisely
In August 2024 the US Department of Homeland Security tested selfie-to-ID matching and impostor detection in Track 2 of its Remote Identity Validation demonstration. Of sixteen systems, HyperVerge alone met every benchmark: at least 90% true positives and at most 1% false positives. It covers that test only, at one point in time.
Injection attacks are the part to ask about
Presentation attack detection checks whether the camera sees a live person; an injection attack bypasses the camera, feeding synthetic video through a virtual camera or tampered client. ISO 30107-3 Level 2 tests presentation only, so device intelligence must catch emulators and virtual cameras. Ask about injection separately.
Deduplication catches what per-application checks cannot
Document, liveness and face-match checks judge one application at a time, so a fraud ring passes them with the same real person opening the fifteenth account. Face deduplication matches each applicant against everyone enrolled, surfacing a loss category existing controls structurally could not see.
What a certification does not promise
The DHS result and iBeta ISO 30107-3 certification are point-in-time tests against known attacks, not permanent guarantees, as generative models keep improving. Ask how often models are retrained, how fast new attack techniques reach production, how injection is handled and how often it is re-certified.
The numbers behind the platform
What your fraud rollout looks like
Name the fraud you are actually losing to
Synthetic identities, forgery, repeat applications and account takeover need different controls. Start from your loss data.
Ask the injection question
Ask about injection separately from liveness: ISO 30107-3 covers presentation attacks, and a virtual camera presents nothing.
Run dedup against your existing base
Matching new applicants against everyone enrolled surfaces losses your per-application checks could never see.
Tune thresholds on real traffic
Too tight floods manual review; too loose defeats the point. Allow a couple of months of real volume to tune thresholds.
Wire fraud signals into the decision
A fraud signal at onboarding should reach the credit decision and the review queue, not sit in its own dashboard nobody opens between incidents.
Track the model-update cadence
How fast a newly observed attack technique reaches production matters more over three years than the day-one benchmark.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Deduplication found a ring running fifteen accounts off variations of one face, each of which had passed our checks.”
“The DHS Track 2 result got them shortlisted: a real test with published thresholds, unlike most vendor claims.”
“Ask about injection attacks specifically. Liveness certification covers presentation attacks, not virtual cameras.”
“Good detection, but our first thresholds sent too much to manual review, and tuning took a couple of months of traffic.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and review-site grids score satisfaction, not fit for your estate. So here’s TechBag’s synthesis of the identity fraud prevention market — tap any vendor to see why it sits where it does.
TechBag Fraud Prevention Grid
Execution strength vs product vision — the classic market map, minus the paywall.
DHS Track 2 sole pass; ISO 30107-3 L2.
Journey Breadth × Benchmark Evidence
The grid nobody publishes — breadth across the onboarding journey vs the strength of measured benchmark evidence.
Measured benchmarks, on one journey.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Fraud Prevention vs the alternatives
Against a global IDV vendor, rules with manual review, and nothing beyond KYC — on measured evidence, injection and dedup.
| Dimension | HyperVerge Fraud Prevention | A global IDV vendor | Rules and manual review | Nothing beyond KYC |
|---|---|---|---|---|
| Deepfake / liveness evidence | DHS Track 2 sole pass | Varies, often certified | Human judgement | None |
| Injection attack handling | Device intelligence | Varies | No | No |
| Cross-application dedup | Face deduplication | Usually available | If someone notices | No |
| AML in the same journey | Yes | Usually | Batch | No |
| India data residency | Not stated | Varies | Your servers | — |
| Published pricing | Quote-only | Varies | Staff cost | Free |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose HyperVerge Fraud Prevention if…
- Deepfakes and synthetic identities are a live threat rather than a future one
- You want measured benchmark evidence — the DHS Track 2 result is unusually concrete
- Repeat and organised fraud is getting through checks that pass each application alone
- Onboarding already runs here, so fraud signals share the same journey
Compare a global IDV vendor if…
- You need consistent fraud coverage across many countries, not primarily India
- Procurement requires an analyst placement — HyperVerge holds none
- A published India data-residency commitment is non-negotiable for you
Do not expect…
- A certification to be a permanent guarantee — every one is point-in-time
- Liveness certification to answer the injection question; ask about that separately
- Zero manual review — budget a tuning period before thresholds settle
What does identity fraud cost you?
Drag the sliders (monthly verification volume; average fraud loss per incident). Estimates model losses from synthetic identities and repeat applications that per-application checks pass individually. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Quote-only — HyperVerge publishes no price, and per-check cost varies by type. TechBag scopes the check mix including the tuning ramp, then quotes in INR with GST.
Fraud Prevention
Best when deepfakes are already live
- DeepfakeSafe and ISO 30107-3 L2 liveness
- Dedup across your whole enrolled base
- AML screened inside the journey
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ the wider platform
Best across the journey
- Fraud signals reach the credit decision
- One journey, not a separate integration
- Onboarding and fraud share the same trail
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Which fraud type is actually costing you? Synthetic identity, forgery, repeat applications and takeover need different controls.
How are injection attacks handled, specifically? ISO 30107-3 certifies presentation attacks; a virtual camera is a different problem.
When was the liveness certification issued, and what is the re-certification cadence? Point-in-time results age.
How quickly does a newly observed attack technique reach production? Over a three-year contract this beats a day-one benchmark.
Is dedup against your whole enrolled base, and is it included in the quote? It is often priced separately.
Who works the exception queue during tuning? Thresholds take real traffic and a couple of months to settle.
Where is biometric data stored? Nothing is published, and biometric data carries its own sensitivities under DPDP.
Which checks are billed, and at what rate each? Per-check pricing varies by type and nothing is published.
Questions buyers ask
Ready to evaluate HyperVerge Fraud Prevention?
Run deduplication against your existing base first — that is where pilots surprise people — or let a TechBag advisor get the injection-attack and residency answers in writing.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.