Seatext library / BotRefund evidence
Which Methods Does BotRefund Employ Beyond Simple IP Blocking?
BotRefund replaces IP blocking with 106 independent checks grouped into hardware fingerprinting, behavioral biometrics, and an AI prediction engine that weighs the full pattern. Each check adds one piece of evidence; the model only...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
BotRefund does not rely on IP addresses to separate humans from bots. Instead it runs 106 independent checks that fall into three layers: hardware and GPU fingerprinting, behavioral biometrics, and an AI prediction engine that cross-references every signal before reaching a verdict. A single anomaly is never treated as proof; the system requires corroboration across browser, network, device, and behavior data.
Why IP blocking alone fails
Attackers rotate residential proxies, hijacked IoT devices, and VPN exit nodes faster than any blocklist can update. The source pack notes that fraud networks now route clicks through "networks of hijacked smart devices (IoT) in target local areas" so the ad platform sees legitimate residential IPs [S8]. IP reputation also produces false positives when corporate networks, shared offices, or travelers share a single address. BotRefund therefore treats IP data as one weak signal among many, not a decision rule.
The three detection layers BotRefund uses
Every visit passes through three complementary layers. Each layer produces independent evidence that the AI model weighs together.
Layer 1: Hardware and GPU fingerprinting
BotRefund interrogates the browser's rendering stack. The WebGL Texture Constraint check compares the reported GPU, driver, font list, and audio stack against the known profile for that device class [S1]. A virtual machine or spoofed profile often claims one device while its graphics, fonts, or processor behavior tell another story. Other checks in this family examine canvas rendering, audio context, and WebGL parameter consistency. The source pack describes this as "Hardware & GPU Fingerprinting" and notes it is "one of 106 independent checks BotRefund uses to build a reliable picture" [S1].
Layer 2: Behavioral biometrics
Real users move the mouse with micro-tremors, pause to read, hesitate before clicking, and scroll in curved paths. Bots—even AI-enhanced ones—struggle to reproduce that variability. BotRefund measures:
- Pointer behavior: robotic linear movements vs. natural curves; absence of humanlike mouse tremor [S2].
- Click behavior: ghost clicks that lack the natural intent sequence; honeypot trap interactions with hidden page elements [S2].
- Speed behavior: superhuman input speed under 1 millisecond [S2].
- Path behavior: grid-aligned movement that snaps to precise lines instead of natural curves [S2].
- Engagement behavior: absence of clicks or scrolling; sessions that stay too static [S2].
- Session behavior: unnatural durations—too short, too long, or too uniform [S2].
- Navigation behavior: impossible tab-switching speed and window.open tampering that reveal scripted navigation [S6][S7].
The source pack groups these under "Biometric & Behavioral Interactions" and emphasizes that "a real visitor produces imperfect, varied behavior: pauses, hesitation, natural movement, and interactions shaped by reading and decision-making" [S6].
Layer 3: AI prediction engine with cross-signal corroboration
Each of the 106 checks contributes one objective fact. The AI model then tests whether other signals support the same story. The source pack outlines the three-step logic: "Independent evidence — This signal adds one objective fact about the visit. Cross-checked context — BotRefund tests whether other signals support the same story. AI prediction — Our model weighs the complete pattern instead of trusting a raw rule" [S1]. The claimed result is 99% accuracy derived from corroboration, not from any single browser tell [S1].
How the 106 checks work together in practice
When a visitor lands, the JavaScript sensor collects hardware, network, and behavioral telemetry in parallel. Each check returns a normalized anomaly score. The AI model ingests the full vector and outputs a bot probability. If the probability crosses the decision threshold, the visit is flagged and the click ID (GCLID or FBCLID) is logged for refund evidence [S8]. The system also generates audit-ready dispute reports that ad-platform reps accept [S5].
A hypothetical e-commerce site spending $200,000 per month on Google and Meta might see 14% of clicks flagged as bots. In the FinTrust case study, suppressing those conversion events lifted the reported conversion rate by 18% and recovered $140,000 in ad spend [S5]. The same logic applies to lead-generation funnels where affiliate fraud inflates CPL commissions with auto-generated signups [S9].
Decision framework: choosing a bot-detection approach
Use the table below to compare BotRefund's multi-layer method against common alternatives. The criteria reflect what a buyer can act on: setup effort, evidence quality, refund support, and ongoing maintenance.
| Criterion | BotRefund (106 checks + AI) | IP blocklist only | Basic WAF rules | Managed bot-protection service |
|---|---|---|---|---|
| Setup effort | One-minute script install; no credit card [S2] | Low (firewall rule) | Medium (rule tuning) | High (integration, training) |
| Evidence quality | 106 independent signals; hardware, behavior, network [S1] | Single signal (IP reputation) | Few heuristic rules | Vendor-dependent; often opaque |
| False-positive control | Cross-checked context; single anomaly never a verdict [S1] | High (shared IPs, VPNs) | Medium (rigid rules) | Varies; check with vendor |
| Refund-ready proof | Click IDs logged; video capture; audit reports accepted by Meta/Google reps [S5] | None | None | Check with vendor |
| Ad-platform integration | Google Ads & Meta pixel protection; GCLID/FBCLID capture [S8] | None | None | Check with vendor |
| Ongoing maintenance | Model updates automatically; 106 checks evolve [S1] | Daily blocklist updates | Rule rewrites per attack | Vendor handles; SLA-dependent |
Choose BotRefund if you need refund-grade evidence for Google and Meta, want near-zero setup, and prefer a model that self-updates as fraud tactics shift. Choose a managed service if you have a dedicated security team that wants full rule control and can negotiate custom SLAs. Avoid IP blocklists or basic WAF rules as your only defense—they miss residential-proxy bots and generate costly false positives.
Limitations and when this approach does not apply
- Client-side only: The sensor runs in the browser. Server-side API abuse, credential stuffing via headless browsers that execute full JS, or sophisticated device farms that pass all 106 checks may still slip through.
- Privacy tools: Hardened browsers (Tor, Brave with fingerprinting protection) can mask or randomize hardware signals, creating noise the model must weigh carefully.
- Non-ad traffic: The refund workflow is built for Google and Meta click IDs. Organic, direct, or non-tracked channels do not generate recoverable evidence.
- Accuracy claim: The 99% figure comes from the vendor's internal model evaluation [S1]. Independent third-party benchmarks are not provided in the source pack.
Key facts
| Fact | Detail | Source |
|---|---|---|
| Total independent checks | 106 | S1 |
| Detection layers | Hardware/GPU fingerprinting, behavioral biometrics, AI prediction | S1, S2 |
| Core hardware check example | WebGL Texture Constraint — mismatched GPU, fonts, audio stack | S1 |
| Core behavioral checks | Mouse tremor, linear vs. curved paths, click intent sequence, honeypot interaction, sub-millisecond speed, grid-aligned movement, scroll absence, session duration anomalies, tab-switch speed, window.open tampering | S2, S6, S7 |
| Decision logic | Independent evidence → cross-checked context → AI pattern weighting | S1 |
| Claimed accuracy | 99% via corroboration | S1 |
| Refund coverage | Google Ads & Meta; click IDs logged back to 2017 | S2, S5 |
| Setup time | About one minute; no credit card | S2 |
| Case-study result | FinTrust: $140k refunded, 14% bot click rate, +18% conversion rate | S5 |
FAQ
Does BotRefund block bots in real time or only report them?
The source pack emphasizes detection, evidence capture, and refund recovery. Real-time blocking at the edge is not described; the workflow centers on logging click IDs, generating audit reports, and suppressing conversion pixels so ad platforms retrain on clean data [S5].
How does the system handle privacy-focused browsers that randomize fingerprints?
The cross-checked context step treats a single anomaly as evidence, not a verdict. If a hardened browser masks WebGL but behaves humanly in mouse movement, scrolling, and session duration, the AI weighs the full pattern and typically classifies the visit as human [S1].
Can I use BotRefund without running Google or Meta ads?
The refund mechanism requires GCLID or FBCLID parameters. Without those click IDs, the platform still detects bots but cannot automate platform disputes. The source pack does not describe a standalone analytics-only mode.
What happens if a legitimate user is flagged as a bot?
Because the model requires multiple corroborating signals, false positives are designed to be rare. The source pack states: "Privacy tools, travel, corporate networks, and unusual devices can produce unexpected behavior for genuine people. BotRefund keeps this signal as evidence—not a verdict" [S1]. No appeal or override process is detailed in the provided sources.
How often are the 106 checks updated?
The source pack says the AI model evaluates the complete picture and implies continuous learning, but it does not publish a release cadence. The checks evolve as fraud tactics shift—e.g., AI-powered bot telemetry and residential proxy expansion are noted as current trends [S8].
Is there a volume threshold below which BotRefund is not cost-effective?
The pricing tiers start at "Under $10,000/mo" ad spend [S2]. The free bot audit is offered regardless of spend, so you can measure the bot rate before committing.
Does BotRefund integrate with analytics platforms like GA4 or Adobe?
The source pack mentions logging click IDs and generating dispute reports [S8]. Direct GA4 or Adobe Analytics integration is not described.
Further reading and comparison sources
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