Seatext library / BotRefund evidence
Common Mistakes When Using BotRefund for Headless Browser Detection
BotRefund detects headless browsers through 106-plus independent checks — such as Playwright init scripts, scrollbar width leaks, and clean-context iframe tests — but each signal is evidence, not a verdict. The most common mistakes...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
BotRefund identifies headless browsers by running over 106 independent checks — including Playwright init script analysis, scrollbar width leaks, and clean-context iframe tests — and feeding every signal into an AI model that weighs the complete pattern across browser, network, device, and behavior data. A single anomaly is never a bot verdict; it becomes one piece of corroborated evidence. The platform's 99 percent accuracy comes from this cross-checked approach, not from any one tell.
Teams that treat a lone signal as a block decision, ignore the cross-validation layer, or fail to export the session-level evidence in the format ad platforms require will miss real bots and waste budget on false positives. Below are the practical mistakes that reduce BotRefund's effectiveness and how to avoid them.
Why Headless Browser Detection Matters for Ad Protection
Headless browsers — automated Chrome, Firefox, or WebKit instances driven by Playwright, Puppeteer, or Selenium — are the primary tool for click fraud, impression fraud, and pixel poisoning on Google and Meta. They load pages, execute JavaScript, and mimic human clicks without a real person behind them. When this traffic hits your landing pages, it inflates costs, corrupts conversion data, and skews bidding algorithms. BotRefund's job is to catch that traffic at the browser level, preserve the click IDs and campaign context, and package the evidence so Google and Meta reviewers can approve a refund.
How BotRefund's Multi-Signal Approach Works
BotRefund runs 110-plus behavioral, browser, hardware, network, and attribution signals on every session. Each check — such as the Playwright init script test, the scrollbar width leak, or the clean-context iframe probe — adds one objective fact about the visit. The system then cross-checks whether other independent signals support the same story. Finally, an AI prediction model evaluates the complete pattern instead of trusting a raw rule. This corroboration chain is why BotRefund reaches 99 percent confidence in the bot traffic it flags.
Common Mistake: Treating a Single Anomaly as a Bot Verdict
Privacy tools, corporate networks, unusual devices, and travel can all produce unexpected browser behavior for genuine visitors. If you block or flag a session based on one failed check — for example, a Playwright init script mismatch — you will generate false positives. BotRefund explicitly keeps each signal as evidence, not a verdict, and only the AI-weighted pattern produces the final classification. Configure your workflow to review the full signal cluster before taking action.
Common Mistake: Skipping Cross-Validation Across Signal Types
The platform tests browser consistency (init scripts, iframe context), device fingerprints (scrollbar width, canvas, WebGL), network context (IP reputation, data-center ranges), and behavior (mouse tremor, click timing, scroll patterns). A headless browser that spoofs one layer often fails another. Teams that only monitor browser-level signals miss bots that pass the browser checks but reveal themselves through superhuman input speed or grid-aligned mouse paths. Use the full signal dashboard; do not disable categories to simplify the view.
Common Mistake: Not Exporting Refund-Ready Reports
Detection alone does not recover money. Google and Meta require structured evidence: click IDs (GCLID, FBCLID), campaign details, timestamps, session recordings, and signal-by-signal reasoning. BotRefund generates reports in the exact format those review teams expect. A common error is reviewing the dashboard internally but never exporting the claim package, or exporting a raw security log that the ad platform cannot parse. Schedule regular report exports aligned with your billing cycles and refund request windows.
Common Mistake: Ignoring Behavioral and Network Context
Headless detection is stronger when paired with behavioral signals — absence of humanlike mouse tremor, superhuman input speed under one millisecond, grid-aligned movement patterns, and unnatural session durations — and network signals such as known data-center IP ranges or VPN exit nodes. Teams that focus only on browser fingerprinting miss bots that use residential proxies and stealth plugins but still behave like scripts. Enable the full 110-plus signal set and let the AI model weigh the combination.
Common Mistake: Failing to Act on Detection Data in Real Time
BotRefund can block pixel poisoning in real time and protect conversion pixels from contaminated data. If you only review reports weekly, your bidding algorithms have already optimized toward fraudulent conversions. Connect the detection feed to your tag manager or conversion API so that flagged sessions are excluded from pixel fires immediately. This preserves the integrity of your optimization signals while the refund claim is prepared.
Key Facts
| Fact | Detail | Source |
|---|---|---|
| Independent checks | 106-plus browser, device, network, and behavior checks | S1, S3, S4 |
| Overall signal count | 110-plus behavioral, browser, hardware, network, and attribution signals | S2 |
| Detection confidence | 99 percent confidence in flagged bot traffic | S1, S2, S3, S4 |
| Refund success rate | 83 percent of clients recover funds from Google and Meta | S2 |
| Brands audited | 2,500-plus | S2 |
| Report format | Refund-ready with click IDs, campaign details, timestamps, session recordings, signal-by-signal reasoning | S2 |
| Signal philosophy | Each signal is evidence, not a verdict; cross-checked against independent data; AI weighs complete pattern | S1, S3, S4 |
Limitations and When This Advice Does Not Apply
BotRefund is built for advertisers who need to prove invalid traffic to Google and Meta and recover spend. It is not a replacement for edge infrastructure such as a WAF, CDN, or DDoS mitigation layer. If your primary need is blocking malicious requests before they reach your origin server, you still need a network-level solution. The detection accuracy figures apply to the platform's AI-weighted pattern across its full signal set; individual checks in isolation have higher false-positive rates. The 83 percent refund recovery rate reflects historical client outcomes across 2,500-plus audits and is not a guarantee for any single account.
FAQ
Can I rely on just the Playwright init script check to block headless browsers?
No. That check is one of over 106 independent signals. A single anomaly is not a bot verdict; privacy tools and unusual devices can trigger it for real users. BotRefund only classifies a visit as automated after cross-checking browser, network, device, and behavior evidence through its AI model.
What happens if I disable some signal categories to reduce noise?
You reduce the corroboration power that drives 99 percent confidence. Headless browsers that spoof browser APIs often fail behavioral or network checks. Keeping the full signal set active lets the AI model weigh the complete pattern.
How do I turn a detection into a refund from Google or Meta?
Export the refund-ready report from BotRefund. It includes click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning in the format the platform review teams expect. Submit that package through the ad platform's invalid traffic claim process.
Does BotRefund block bots in real time or only report them?
It can do both. The platform blocks pixel poisoning in real time and protects conversion pixels. You can also connect the detection feed to your tag manager or conversion API to exclude flagged sessions from pixel fires immediately.
What if my traffic comes through a corporate VPN or privacy browser?
Those environments can produce anomalies on individual checks. Because BotRefund cross-validates across independent signal types and uses an AI model that weighs the full pattern, legitimate visitors on VPNs or privacy browsers are rarely misclassified when the complete evidence set is considered.
Is BotRefund a replacement for Cloudflare or a WAF?
No. BotRefund operates at the marketing layer — onsite behavioral investigation, conversion-signal protection, and refund-ready reporting. It does not provide DDoS mitigation, CDN delivery, or edge WAF rules. Many advertisers run both: an edge layer for infrastructure protection and BotRefund for ad-quality evidence.
How often should I export refund reports?
Align exports with your billing cycles and the ad platforms' claim windows. Google and Meta typically review invalid activity within a rolling 30- to 60-day window. Monthly exports keep your evidence fresh and your claims within the review period.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
How BotRefund can help
BotRefund runs 110-plus independent signals — browser fingerprinting, device consistency, network context, and behavioral biometrics — on every session. Each check adds one objective fact; the AI model then weighs the complete pattern across all signals to reach 99 percent confidence. The platform outputs refund-ready reports with click IDs, campaign details, timestamps, session recordings, and signal-by-signal reasoning in the exact format Google and Meta reviewers expect. Across 2,500-plus audits, 83 percent of clients have recovered funds. The system also blocks pixel poisoning in real time so your bidding algorithms optimize on clean data. It does not replace a WAF, CDN, or DDoS layer; it adds the marketing evidence layer that infrastructure tools do not provide.