What is the difference between FlareSolverr detection and basic browser fingerprinting?

Direct Answer: Basic fingerprinting looks at static browser details, while FlareSolverr detection looks for specific automation remnants like shortcuts left by the toolkit. BotRefund uses the FlareSolverr Patch check as one of 106 independent signals. It combines that evidence with other checks and AI...

Direct Answer: General invalid traffic is basic bots, known crawler user agents, and data center IPs that Meta usually catches. Sophisticated invalid traffic uses residential IPs, real devices, and human emulation to bypass automated filters. SIVT must be identified through client-side behavioral analysis. BotRefund...

Direct Answer: Independent evidence is one objective fact about a visit, like a Background Navigation mismatch. A bot verdict is a conclusion that comes after cross-checking multiple signals. BotRefund treats each signal as evidence, not proof. The verdict is only made when the complete...

Direct Answer: Independent evidence is one fact about a visit, such as a weird scroll pattern or browser detail. A bot verdict is the final decision that a visit is automated. BotRefund keeps each signal as evidence and does not call a bot based...

Direct Answer: Independent evidence is a single objective fact, such as an Iframe Webdriver Leak mismatch, that adds context about a visit. A bot verdict comes only after that evidence is cross-checked against other signals and weighed by AI prediction. BotRefund treats each check...

Direct Answer: Independent evidence is one objective fact about a visit, such as a blocked challenge iframe mismatch. Cross-checked context means BotRefund tests whether other signals support the same story. A single clue is only the starting point. The final classification comes from an...

Direct Answer: IP blocking relies on static blacklists, which modern botnets can easily bypass with rotating proxies. Client-side detection analyzes behavior and browser signals in real time. BotRefund uses 110+ on-site telemetry signals to identify bots that IP blocking misses. This approach also provides...

Direct Answer: IP blocking relies on blocklists that rotating residential proxies easily bypass. Client-side detection inspects the active visitor's device and behavior, providing a more reliable way to catch sophisticated bots. BotRefund uses client-side detection to suppress pixels on bot sessions.

Direct Answer: Network-level filtering like IP blocklists can be easily bypassed by modern bots using rotating residential proxies. Client-side detection, like BotRefund's, inspects the visitor's device and interaction dynamics—such as mouse acceleration, touch pressure, and headless browser attributes—which are much harder for bots to...

Direct Answer: Rule based systems often flag a visit as bot based on a single tell like straight line mouse movement. BotRefund uses an AI prediction model that weighs 106 independent checks including mouse path browser APIs network signals and device fingerprints. The model...

Direct Answer: Rule-based bot detection flags visits based on a single predefined signal, like an IP address or one browser anomaly, which often leads to false positives. AI-based detection, like BotRefund's model, weighs the complete pattern of 106 independent signals to determine if a...

Direct Answer: Rule-based detection might flag a visit the moment one signal looks wrong, which leads to false positives. AI-based detection like BotRefund weighs the complete pattern across browser, network, device, and behavior evidence. The Font OS Mismatch check becomes just one input into...

Direct Answer: Rule-based detection trusts a raw signal by itself, while AI-based detection weighs the complete pattern. BotRefund sends all 106 signals into a prediction AI that evaluates browser, network, device, and behavior evidence together. This approach is what supports the 99 percent accuracy...

Direct Answer: This check looks for mismatches between reported device attributes such as hardware, graphics, fonts, and operating system. Real browsers present consistent information, while virtual machines or spoofed profiles often show contradictions. Detecting these anomalies helps flag automated browsers.

Direct Answer: The Error Stack Proxy check looks for a mismatch in browser APIs that a real browsing session does not normally create. Automation tools often patch or hide browser APIs, but those changes can break when the browser is checked from another angle....

Direct Answer: This category includes checks designed to catch bots that try to evade detection or hide automation. The Iframe Webdriver Leak check is one example used by BotRefund. It looks for API mismatches that automated browsers produce when their patches break under inspection....

Direct Answer: The ExposeFunction Leak check looks for mismatches in browser API behavior that real browsers don't normally produce. Automated tools often patch or hide browser APIs to mask their automation, but these patches can break when the browser is checked from a different...

Direct Answer: First, log FBCLIDs server-side on every landing page visit. Then deploy client-side behavioral analysis to capture bot signals. Next, build an evidence package with FBCLIDs, timestamps, anomalies, and charge amounts. Finally, submit the dispute through Meta Business Help Center. BotRefund automates most...

Direct Answer: It is used to spot automated browsers that try to behave like people reading a page. A real visitor shows pauses and decision-making while scrolling, while a scraper often sends interactions more quickly and uniformly. BotRefund treats this mismatch as one objective...

Direct Answer: The page recommends preserving attribution first, then comparing Meta reporting with web analytics, server logs, call records, email verification, and CRM stages before you touch targeting or budgets. Keep campaign, ad set, creative, placement, click identifier, landing-page URL, and timestamp with each...