Stop click fraud and wasted ad spend
Identify anonymous clicks designed to drain pay-per-click campaign budgets
Ad Fraud Prevention
Prevent ad fraud and protect marketing spend
Stop click fraud, invalid traffic, and anonymous ad clicks before they drain marketing spend, through persistent identification and real-time risk scoring
How ShieldLabs helps
Reach real people, drive real leads
Filter click farms and anonymous clicks before lead quality drops
Trust marketing analytics again
Stop fake clicks from poisoning CAC, ROAS, and cohort analytics
Detect invalid traffic in real time
Score clicks the moment they land, before attribution settles
Scale paid campaigns without risk
Spot fraud share per channel and reallocate before budget commits
End refund disputes with ad partners
Document every invalid click with signal-level evidence for refunds
Everything you need for ad fraud detection
A complete ad fraud prevention solution with real-time analytics across every visitor, channel, and High-Risk Event
See how much of the ad traffic is real
- Traffic Quality Overview
A single quality score showing what share of incoming ad traffic is anonymous and what share is real
- Risk Signals In One View
Surface VPN, proxy, geolocation spoofing, anti-detect browser usage, and other risk signals on every visit
- Accurate Identification
Identify returning visitors and users across sessions, rotated IP and incognito mode even when they cycle ad clicks under new identities
- Real Audience, Finally Visible
See countries, devices, and browsers behind real ad clicks, separated from the noise of anonymous traffic
- Risk Score
A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it
Spot ad fraud per ad platform and campaign
- See The Source Behind Every Visit
View traffic by Channel, Referrer, or UTM Parameter with a per-source traffic risk score on every row
- Cost Per Real Visitor, Not Per Click
Pair ad spend with anonymous-traffic share per source so CAC reflects real people, not just clicks
- Decisions You Can Defend
Scale and reallocate budget based on per-channel and per-UTM risk data no standard analytics gives
Follow ad fraud down to the single click
- Ready-Made High-Risk Events
High-Risk Events detected out of the box: multi-accounting, account sharing, impossible travel and account takeover, each with Medium or High confidence
- One Identity, Many Clicks
Link clicks from the same visitor and user across multiple campaigns into one fraud ring through a persistent identifier
- Investigate Every Suspicious Visit
Search full identification data filtered by risk score to find and review the riskiest ad clicks fast
- Measure How Deep The Abuse Runs
Track High-Risk Events per visitor and user to focus on the clearest threats first
- Evidence For Refund Disputes
Export the full signal trail per flagged click for ad partner refunds
Start preventing ad fraud in 5 minutes
Easily integrate into any landing page, ad-tracking flow, or attribution pipeline
- 1
Create your account
Get 5,000 free identifications
- 2
Add the snippet
It identifies every visitor and returns their risk signals and a risk score
- 3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
- 4
Use the API and Webhooks
Get the risk score, risk signals and High-Risk Events in your backend to stop fraud and abuse
api.shieldlabs.ai/v1/visits/latest
{
"request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
"visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
"device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
"public_ip": { "ip": "62.197.149.124", "country": "United States" },
"local_ip": { "ip": "45.83.91.7", "country": "United States" },
"connection_type": "vpn",
"traffic_source": {
"channel": "paid",
"utm_source": "google",
"utm_medium": "cpc",
"utm_campaign": "brand"
},
"risk_score": 70,
"signals": [
{ "name": "antidetect_browser", "weight": 60 },
{ "name": "proxy", "weight": 10 }
]
}Start preventing ad fraud today
Free 5,000 one-time identifications, with transparent pricing that scales with your needs
Frequently asked questions
Ad fraud is invalid traffic that mimics real user behavior to drain advertising budgets and pollute attribution data. It is an umbrella term covering click fraud, impression fraud, install fraud, and domain spoofing. ShieldLabs scores every ad click on the first visit with 300+ device, browser, and network signals, surfacing the anonymity behind invalid traffic before attribution data is recorded.
The most common types of advertising fraud are click fraud, impression fraud, install fraud, and domain spoofing, and all four share one tell: the device, browser, or network behind the click does not match a real human visitor.
- Click fraud, paid clickers or scripted click activity inflating CPC campaigns.
- Impression fraud, fake views, often through ad stacking or pixel stuffing.
- Install fraud, fake mobile app installs that claim attribution credit.
- Domain spoofing, low-quality sites disguised as premium inventory.
ShieldLabs surfaces that cross-layer mismatch through risk signals and High-Risk Events on every identification, so you can see which clicks came from real people.
ShieldLabs scores every ad click for anonymity the moment it lands, across Google Ads, Meta, TikTok, LinkedIn, X, or any other paid channel, so your team can filter invalid traffic before it reaches attribution. It works the same way on every channel:
- Install one JavaScript snippet on the landing page reached after any ad click.
- Each click receives 300+ device, browser, OS, IP, and network signals surfacing VPN, proxy, datacenter and residential ISP egress IP, anti-detect browser usage, incognito mode, and other risk signals.
- Every visitor and user gets a risk score from 0 to 100 with a full signal breakdown and UTM source attached.
ShieldLabs gives marketing teams the data to filter high-risk clicks out of conversion attribution, reallocate spend away from channels with high invalid-traffic shares, and document invalid clicks per partner, without waiting for ad-network refund cycles.
Click fraud is one specific type of ad fraud; ad fraud is the umbrella term for every scheme that produces invalid traffic. The distinction is one of scope:
- Click fraud, artificially generating clicks on pay-per-click ads to drain budgets or inflate competitor costs.
- Ad fraud, the broader category, also covering impression fraud, install fraud, attribution fraud, and domain spoofing.
ShieldLabs surfaces both: it scores any click or impression where the visitor behind it is anonymous or running an anti-detect browser, so you can tell invalid traffic from real people regardless of the scheme.
ShieldLabs scores ad fraud at the visitor and user level through persistent identification, where most ad fraud tools either chase PPC click-tax refunds or sell sales-led viewability suites. The category splits three ways:
- Click-tax tools, file invalid-click reports to Google and Bing to claw back PPC spend, but stop at the click.
- Ad-verification platforms, focus on viewability and brand safety for large advertisers and require sales-led contracts.
- ShieldLabs, surfaces a transparent 0–100 risk score and risk signals on every click, tied to a persistent identifier so the same actor is recognized across campaigns and rotated IPs.
ShieldLabs gives enterprise-level functionality without enterprise pricing: the full signal set is self-serve from $99 a month.
No. ShieldLabs helps block fraudulent and abusive traffic, and a VPN on its own does not make a visitor risky. Every click comes back with a Risk Score and signal Details.
- A VPN on its own is a low-weight signal: real users on iCloud Private Relay, corporate VPNs, or privacy tools stay low-risk.
- High scores require multiple signals firing together, such as an anti-detect browser plus a cross-layer OS mismatch plus a datacenter egress IP.
- The free 5,000 identifications let you see how your own traffic scores first.
ShieldLabs gives you the score and the evidence, and you choose the action for each click: challenge, ignore, or discount it.
Industry research estimates digital ad fraud losses at over $100 billion globally in 2025–2026, but the share that hits any single advertiser depends entirely on its channel mix. Typical patterns:
- Broad programmatic campaigns, often 20–40% anonymous-traffic share.
- Retargeting and display, variable, usually in between, driven by inventory quality.
- Brand-search campaigns, usually under 5% anonymous-traffic share.
ShieldLabs measures the exact anonymous-traffic share on your own campaigns, so you see real ad-fraud impact per channel instead of relying on industry averages.