Seatext library / BotRefund evidence
How Many Detection Signals Does BotRefund Use?
BotRefund uses 106 independent detection signals to identify automated traffic. These signals are cross-checked against browser, network, device, and behavioral data to provide a 99% accurate assessment of whether a visit is human or...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
Understanding the 106-Signal Detection Process
BotRefund employs 106 independent checks to build a reliable profile of every website visitor. Rather than relying on a single "tell" or rule, the system gathers objective facts about a session and feeds them into a prediction AI. This model evaluates the complete picture to distinguish between genuine human users and automated scripts.
The core of this process is corroboration. Because privacy tools, corporate networks, and unusual devices can sometimes mimic bot-like behavior, BotRefund treats a single anomaly as evidence rather than a final verdict. By cross-referencing hardware, graphics, fonts, and behavioral patterns, the system ensures that legitimate users are not incorrectly flagged.
Each signal contributes one objective fact. For example, the CPU Concurrency Lie check examines whether a browser's reported hardware matches its actual processor behavior. A real browser usually shows a consistent story—the operating system, graphics, fonts, and CPU all align. Virtual machines and spoofed profiles often claim one device while their behavior tells another story. This mismatch is a strong indicator, but not proof by itself.
Another check, the window.open Tamper signal, monitors for manipulation of browser APIs that a normal user would never invoke. Similarly, the Impossible Tab Speed check flags interactions that happen faster than a human could physically perform. These signals are drawn from observed bot behaviors, not guesses.
The system then cross-checks all 106 signals. If a single anomaly appears, it might be a false positive. But if multiple independent signals point in the same direction, the probability of a bot rises sharply. This multi-layered methodology is what gives BotRefund its 99% accuracy rate.
How the Detection Signals Work
The 106 signals fall into several categories. Each category captures a different dimension of a browsing session.
- Hardware & GPU Fingerprinting: Checks for mismatches between reported hardware and actual processor behavior, like the CPU Concurrency Lie. It also examines graphics rendering and font availability.
- Behavioral Interactions: Monitors for robotic movement, such as perfectly linear mouse paths or a lack of human-like jitter. For instance, the pointer behavior check flags unnaturally straight paths, while the motion behavior check looks for the tiny imperfections typical of human tremor.
- Session & Engagement: Analyzes timing, such as superhuman input speed (under 1ms) or unnatural session durations. It also checks for absence of clicks or scrolling, which indicates a static session that does not match real browsing.
- Trap & Tamper Detection: Identifies interactions with hidden honeypot elements or attempts to tamper with browser functions like
window.open. Honeypot traps are invisible elements that only bots tend to interact with. - Click & Path Behavior: Detects ghost clicks (clicks without the natural sequence of human intent), grid-aligned movement patterns, and other non-human input patterns.
Each signal is designed to catch a specific weakness in bot emulation. For example, a bot might spoof a device's user agent, but it may still fail the CPU Concurrency Lie if its processor behavior does not match the reported hardware. Another bot might simulate mouse movement, but it will often produce linear paths instead of the curved, imperfect paths of a real user.
These signals are not static. BotRefund continuously updates them based on new bot tactics and new forms of automation. For instance, the rise of AI-driven bot telemetry—where bots use AI to simulate human-like mouse curvature and scrolling—requires more sophisticated checks. BotRefund responds by adding and refining signals that detect the subtle differences between AI-generated behavior and organic human movement.
Why Single-Signal Detection Fails
Modern bots are highly sophisticated. They often use residential proxies to hide their IP addresses and AI-driven generators to simulate human-like mouse movements and scrolling. If a security system relies on only one or two signals—such as IP reputation or basic browser headers—it is easily bypassed by these advanced tactics.
Consider residential proxy expansion. Fraudsters route clicks through hijacked smart devices and IoT networks in target local areas. This gives the bot traffic legitimate residential IP addresses, making location-based exclusions useless. An IP-only detection system would miss these bots entirely.
Similarly, AI-powered bot telemetry introduces organic-looking irregularities. Bots no longer move in rigid lines; they now generate curved paths and variable click intervals. Simple pattern-detection rules that look for linear movement fail because the bot's movement looks human-like at a single-point check.
A multi-signal approach catches these bots because they cannot fake every dimension. A bot might use a residential IP, but it still cannot perfectly replicate GPU rendering, CPU concurrency, and the complex emotional timing of a human browsing session. By looking at the entire pattern, the AI can identify the bot even when individual components appear legitimate.
For example, a bot might spoof a device's operating system and pass basic header checks. However, it might still fail the "Impossible Tab Speed" check if it switches tabs faster than any human could. Or it might trigger the "window.open Tamper" signal by attempting to open windows without user consent. These small tells, when combined across 106 signals, create a reliable fingerprint of automation.
How the AI Prediction Model Works
BotRefund does not rely on a simple rule of "if two signals match, it's a bot." Instead, it uses a prediction AI that learns from historical data. The AI is trained on millions of sessions—both human and automated—to understand which combinations of signals are most indicative of bot activity.
Each of the 106 signals is assigned a weight. Some signals are more powerful than others. For example, the CPU Concurrency Lie is a strong signal because it involves a complex hardware mismatch that is difficult to fake. The Impossible Tab Speed is also significant. Behavioral signals like mouse tremor carry weight, but they are less definitive on their own because some humans have very steady hands.
The AI model combines these weighted signals into a probability score. It does not just sum up anomalies; it looks at how signals interact. For instance, a single false positive—like a user on a virtual machine with unusual GPU behavior—might not push the score past the threshold. But if that same user also shows superhuman input speed and no engagement, the probability of a bot rises.
The model is continuously retrained with new data. When bot operators change their tactics, the model learns to detect new patterns. This is why the 106 signals are not fixed; they evolve to stay ahead of automation. The AI also adapts to different website types, industries, and user segments, reducing false positives for legitimate but unconventional users.
This approach is what enables BotRefund to claim 99% accuracy. By evaluating the complete pattern across browser, network, device, and behavior evidence, the AI makes a nuanced judgment that a raw rule cannot.
Trade-offs of Using 106 Signals
Running 106 independent checks on every visit has trade-offs. The most obvious is performance impact. Collecting hardware, GPU, behavioral, and session data adds some overhead to the page load. BotRefund minimizes this by using lightweight JavaScript and asynchronous loading. The checks are designed to run without slowing down the user experience for real visitors.
Another trade-off is dealing with privacy tools. Users who block JavaScript, use aggressive ad blockers, or browse in incognito mode may generate missing or altered signals. This can increase false positives. BotRefund handles this by treating those signals as "unknown" rather than as evidence of bot behavior. The AI can still make a decision based on other signals, and the overall accuracy remains high.
False positive mitigation is a central challenge. A corporate network behind a proxy, a user with a high-end gaming mouse, or a person using a screen reader can all produce behavior that looks unusual. BotRefund's corroboration approach prevents a single anomaly from triggering a bot verdict. Instead, the system requires multiple independent signals to align. This reduces the risk of blocking genuine users.
There is also a trade-off between sensitivity and specificity. If the system is too sensitive, it flags too many human users. If it is too specific, it misses sophisticated bots. BotRefund tunes its model to minimize both errors. The 99% accuracy figure reflects a balance where false positives are extremely rare, while still catching advanced threats.
Finally, the 106 signals require continuous maintenance. Bot operators are always developing new evasion techniques. BotRefund invests in research and updates its signal library regularly, so the system remains effective. This is not a one-time setup but an ongoing process.
Key Facts About BotRefund Detection
| Feature | Description |
|---|---|
| Total Signals | 106 independent checks |
| Accuracy | 99% accuracy through corroboration |
| Methodology | AI prediction model weighing complete patterns |
| Evidence | Cross-checks browser, network, device, and behavior |
| Setup Time | About one minute, no credit card required |
These facts are drawn directly from BotRefund's official documentation. The system is designed for speed and accuracy, making it practical for production websites.
The Importance of Behavioral Auditing
Behavioral auditing is critical for protecting ad spend. Bots often target conversion pixels, creating "poisoned" data that leads to poor campaign performance. By auditing behavior, you can suppress automated conversion events, ensuring that platforms like Google and Meta train their AI models only on verified human interactions. This leads to higher-quality leads and more efficient budget allocation.
A case study from BotRefund shows how this works in practice. FinTrust, a neobank, used BotRefund to fight massive bot registration attempts on search ad landing pages. These bots were inflating customer acquisition costs and distorting metrics. After implementing behavioral auditing and suppression, FinTrust recovered $140,000 in ad spend, reduced its average bot click rate to 14%, and increased conversion rate by 18%. The video proof and audit trails were accepted by Meta and Google as evidence for refunds.
Behavioral auditing also helps with lead quality. A fake lead may be designed to earn an affiliate payout, inflate a publisher's performance, or simply exhaust a sales team's time. By examining contactability, timing, session behavior, campaign patterns, and CRM outcomes, BotRefund can identify invalid traffic before it harms your pipeline.
For example, a lead that arrives in a sudden burst, with no scrolling or field corrections, and has a disconnected phone number is likely a bot. BotRefund flags these sessions and prevents them from reaching your CRM or conversion pixel. This protects your data and your ad budget.
Frequently Asked Questions
Does a single anomaly mean a visitor is a bot?
No. BotRefund treats a single anomaly as evidence, not a verdict. It cross-checks that signal against other data points to confirm the visitor's identity.
How long does it take to set up?
You can add BotRefund to your website in about one minute. No credit card is required to start the initial audit.
Can BotRefund help recover money from ad platforms?
Yes. BotRefund detects bot clicks and captures video proof, which can be used to generate audit-ready reports for Google and Meta billing disputes.
What happens if I ignore bot traffic?
Ignoring bot traffic allows automated scripts to consume your ad budget, distort your conversion metrics, and waste your sales team's time with fake leads.
Does this work for all ad platforms?
BotRefund is specifically designed to help recover ad spend from Google and Meta by providing the evidence needed for refund claims.
How do I interpret the audit report?
The report shows a breakdown of signals per session, a confidence score, and video evidence for any flagged bot activity. It also includes a summary of invalid clicks and their estimated cost.
What role does behavioral auditing play in ad spend recovery?
Behavioral auditing provides concrete proof that conversion events came from bots, not humans. This proof is essential when submitting refund claims to ad platforms.
How are signals updated against evolving bot tactics?
BotRefund continuously analyzes new bot behavior from real traffic and research. It updates the signal library and retrains the AI model to detect emerging threats.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
Further reading and comparison sources
These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.
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