If you landed here after searching for something like “browser fingerprint test”, “bot detection API”, or “Kameleo anti-detect browser”, then welcome. The search engine did its job, and so did we.
Let’s be honest. The internet is full of low-quality SEO pages trying to rank for every keyword, even remotely related to bot detection or fingerprinting. You’ve probably seen them: hundreds of “What is a bot?” articles written by LLMs on autopilot. They all say the same thing, and none of them explain how these systems really work.
This article is a bit different. Sure, it also contains the same keywords (otherwise you wouldn’t have found it). But here, we actually use them for a reason. Our goal is to make something useful, a guide that helps you understand what these tools do, how they connect to real detection systems, and how we analyze them at Castle.
We study this traffic every day. Our research covers how fingerprinting signals behave across browsers, how privacy tools affect detection accuracy, and how automation frameworks evolve to look more human. So instead of another SEO trap, you’ll find links to our research articles, tutorials, and technical breakdowns: content based on real attacks, real data, and practical experience.
If you just need to know what browser fingerprinting or headless Chrome means, you’ll find it below. But if you want to understand what’s actually happening behind those terms, why fingerprinting still matters in 2025, how anti-detect browsers abuse it, and what it takes to stop modern fraud, we’ll show you where to dig deeper.
So yes, we also played the keyword game. But at least we did it for the right reasons.
Fingerprinting and browser analysis tools
These are online services that help you understand how much information your browser exposes. They show what data is available through browser APIs like Canvas, WebGL, and AudioContext, and sometimes test if your setup looks automated (bot), spoofed, or routed through a VPN.
People usually discover these tools when testing privacy extensions, headless browsers, or anti-detect setups. They’re also useful for security engineers who want to see how consistent or “stealthy” their client environments look.
In our article What browser fingerprinting tests like AmIUnique and BrowserLeaks really show — and what they miss, we explain how these tools work, what they measure correctly, and why many of them fail to detect modern automation or properly evaluate the quality of anti-fingerprinting countermeasures.
If you’re interested in how privacy tools or browser forks may (negatively) impact your browsing experience, check How bot detection misfires on non-mainstream browsers and privacy tools. It covers how detection models can generate false positives when facing rare setups like LibreWolf or hardened Firefox.
For readers who want to go deeper into specific fingerprinting signals, we’ve also published technical guides:
- Deep dive: how
navigator.deviceMemorycan be used for fingerprinting and bot detection - The role of WebGL renderer in browser fingerprinting
- Canvas fingerprinting in the wild
Pixelscan
Pixelscan is a browser fingerprint analysis tool that checks how detectable your browser is. It simulates real fingerprinting scripts and highlights inconsistencies that can reveal automation, spoofed configurations, or headless setups.
BrowserLeaks
BrowserLeaks is one of the oldest and most complete fingerprint testing pages. It lists every bit of data your browser exposes, including WebGL, Canvas, AudioContext, WebRTC IPs, and TLS fingerprinting.
BrowserScan
BrowserScan profiles a browser by collecting attributes like user agent, WebGL, canvas, fonts, plugins, and device settings. It helps detect mismatches or signals that don’t align with a normal environment.
CreepJS
https://abrahamjuliot.github.io/creepjs/
CreepJS is a JavaScript library that collects hundreds of fingerprinting signals directly from the browser. It’s often used by researchers to see how entropy accumulates and how unique a device appears online.
FingerprintJS Playground
https://demo.fingerprint.com/playground
FingerprintJS offers a demo showing how deterministic attributes can be combined into a persistent identifier. It’s a good example of how commercial fingerprinting systems aggregate low-level signals.
AmIUnique
AmIUnique is an academic project that studies how unique browser fingerprints are. It helps visualize how simple combinations of screen, language, and rendering details can make a setup identifiable.
Anti-detect browsers and fingerprint spoofing tools
Anti-detect browsers are designed to hide or modify browser fingerprints. They let users appear as many different devices by changing attributes like the user agent, WebGL renderer, screen size, or timezone. These browsers are often promoted as tools for marketing, scraping, or account management, but they are also used to automate fraud and bypass detection systems.
At Castle, we’ve spent a lot of time studying these tools in the wild. In Overview of anti-detect browsers, we explain how this ecosystem evolved, what kind of users rely on it, and how most of these browsers work under the hood.
For a deeper look at how we detect them, check Anti-detect browser analysis: how to detect the undetectable browser. It breaks down the signals that reveal these browsers, such as hardware inconsistencies, JavaScript quirks, or differences between browser layers.
We also analyzed individual tools to see how well they spoof fingerprints. For example, in Detecting Hidemium: fingerprinting inconsistencies in anti-detect browsers, we show concrete examples of rendering mismatches and logical contradictions inside the browser environment that help identify automation.
These articles are a good starting point if you want to understand how anti-detect browsers interact with modern bot detection systems, and why full invisibility is still impossible in practice.
Kameleo
Kameleo is an anti-detect browser that lets users create multiple browser profiles with customized fingerprints. It’s mainly used for web scraping, traffic arbitrage, and account management, where each session needs to appear as a unique device.
Dolphin Anty
Dolphin Anty is a multi-profile browser used to manage large numbers of accounts. It offers fingerprint customization, proxy integration, and shared profile management.
GeeLark
GeeLark is another anti-detect browser positioned as a privacy solution. It allows users to modify browser attributes and connect through different proxies, making it harder for detection systems to link sessions together.
GoLogin
GoLogin is similar in purpose to Kameleo and Dolphin Anty. It emulates real browser environments and lets users change fingerprints and IPs to appear as separate users. It is commonly used for automation and social media account management.
Before moving on, it’s worth mentioning that anti-detect browsers are often used together with CAPTCHA-solving APIs like CapSolver. These services help bypass the visual or behavioral challenges that still block automated signups or logins.
We analyzed an open source CAPTCHA solver in this article.
CapSolver
CapSolver is an AI-based service that automates CAPTCHA solving. It supports challenges like reCAPTCHA, hCAPTCHA, and FunCAPTCHA and is often integrated into automation frameworks to simulate human interactions.
CAPTCHA and bot detection services
These services are built to identify or block automated traffic. Some focus on IP-based scoring, others on browser or behavioral patterns. They can stop basic scripts and spam bots, but modern automation tools now blend in so well that traditional CAPTCHAs often fall short.
At Castle, we’ve written several guides explaining how modern bot/fraud detection works behind the scenes:
- CAPTCHAs 101: what they are, how they work, and where they fall short
- Bot detection 101: how to detect bots in 2025
- Why traditional bot detection techniques are not enough — and what you can do about it
Together, these articles show how modern detection systems combine multiple layers: IP reputation, fingerprinting, browser integrity checks, and behavioral analysis. Static IP lists can still help, but on their own, they miss most advanced bots that rotate proxies or emulate real browsers.
IPQualityScore (IPQS)
https://www.ipqualityscore.com
IPQualityScore is a commercial fraud detection API that evaluates risk based on IP, device, and browser signals. It estimates the likelihood of malicious activity such as automated logins, click fraud, or fake traffic.
FireHOL IP Lists
FireHOL compiles IP blocklists from open threat feeds. The lists include addresses linked to spam, brute-force, and malware activity. They are often used to improve basic filtering or as an additional signal for traffic scoring.
Spamhaus Blocklist
https://www.spamhaus.org/blocklists/spamhaus-blocklist/
The Spamhaus Blocklist identifies IPs and ranges associated with spam, phishing, or general abuse. It’s still useful for filtering known bad sources, but should be combined with behavioral and fingerprinting checks to detect fast-moving or rotating botnets.
Security and fraud prevention concepts
This section brings together the key ideas behind fraud detection and bot defense.
It explains the concepts you’ll often see in our research: from how attackers take over accounts to how browsers can betray automation through fingerprints or headless execution.
If you want a deeper look at how these attacks work in practice, we’ve written detailed guides:
- Fake account creation attacks: anatomy, detection, and defense
- Credential stuffing attacks: anatomy, detection, and defense
- How we detected a CAPTCHA solver in the wild
Together, these articles show how different layers of detection come together: browser fingerprints, behavioral analysis, and server-side validation. They also highlight the limits of static rules and why continuous adaptation is key when facing real-world attacks.
Account takeover prevention (ATO)
Account takeover happens when attackers gain access to legitimate user accounts using stolen credentials or personal data. Once inside, they often change profile information or perform fraudulent transactions. At Castle, we study how bots and credential stuffing tools automate this process, and how real-time risk scoring can stop it before damage occurs.
Browser fingerprinting
Browser fingerprinting collects small details about the browser and device to identify users, even without cookies. It’s used by both attackers and defenders. Fraudsters use it to track automation success, while detection systems use it to link suspicious sessions together.
What is headless Chrome
Headless Chrome is a version of Google Chrome that runs without a visible interface. It’s controlled by scripts and widely used for automation, scraping, and testing. We often see it in credential stuffing and fake account creation attacks. In our article Inside the real bot battlefield, we explain how headless browsers are used at scale and what signals still expose them despite heavy spoofing.
Disposable emails
Disposable or temporary email services let users create short-lived inboxes to receive messages without revealing their real address. They’re convenient for quick signups or testing, but they also play a big role in automated abuse. Attackers use them to create fake accounts at scale, bypass registration limits, or hide identity links between fraudulent activities.
If you want to go deeper into how disposable email services operate and how to detect them, see our article Understanding disposable emails. It explains how these domains are generated, what infrastructure supports them, and how we group related ones into clusters.
For a more technical view of how we link fraudulent domains, read Finding links between fraudulent email domains using graph-based clustering. It shows how graph analysis can uncover large networks of disposable domains that share hosting, DNS patterns, or usage across attacks.
Emailnator
Emailnator is one of the popular disposable email platforms that provides instant inboxes for receiving messages anonymously. It markets itself as a privacy service, but its domains often appear in automated account creation and credential stuffing attacks.
See our related research: Deep dive: how disposable email services like Emailnator are abused in automation campaigns.
The list below contains several popular disposable email domains
- mail.theloi.io.vn
- fviainboxes.com
- fviadropinbox.com
- smvmail.com
- dropinboxes.com
- tmxttvmail.com
- temp-mail.org
High-entropy content, low marketing noise
If you made it this far, you’ve probably noticed this isn’t another keyword-stuffed “bot detection guide.” We used the same search terms everyone else does, but our goal was different: to make them actually useful.
At Castle, we focus on clear, research-based writing, the kind that helps you understand how detection really works instead of repeating definitions for search rankings.
We could have added more buzzwords, but we’d rather show how fingerprinting, anti-detect browsers, disposable emails, and fraud detection connect in practice.
Thanks for reading. Hopefully, this helped you find something written for people, not algorithms.
