Anti-detect browsers emerged as a response to the spread of browser fingerprinting technologies – the covert identification of users based on a combination of their device’s parameters and environment. Modern websites, besides using cookies, track IP addresses, geolocation, and dozens of browser characteristics (such as Canvas, WebGL, the list of fonts, User-Agent, etc.) to distinguish and link visitors. As a result, even when in incognito mode or after changing one’s IP, a user can be detected by their “digital fingerprint” – a unique set of properties of their browser.

In fact, when I first started my journey in these internet realms, my expertise in digital security was evolving—and continues to grow—and I eventually came to understand browser fingerprints. At first, I believed cookies—collected by those pesky search engines that tracked what I viewed—were to blame, then I learned about browser fingerprints and long denied that I needed to learn to work with and understand them. Really, just when you finally figure out proxies, learn how to change and preserve cookies, here comes a new twist. Moreover, it turns out that fingerprints are also sold, and the price is not exactly low. In short, money is made on everything! But that’s beside the point now!

An anti-detect browser is a modified browser (often based on Chromium or Firefox) that substitutes or masks these properties (fingerprints), preventing websites from unequivocally identifying the user and detecting multi-accounting.

With an anti-detect browser, a dozen different accounts appear as ten independent users with distinct devices—even though, in reality, they all operate from the same computer. This opens up new possibilities on the internet, ranging from safe browsing to business tasks that involve managing multiple accounts.

Application of Anti-detect Browsers

Initially, such solutions gained popularity in the niche of affiliate marketing and traffic arbitrage, where it is necessary to create numerous advertising accounts and bypass bans on advertising platforms. Today, the range of applications is broad: multi-accounting in social networks, SMM and client account management, mass posting of advertisements, e-commerce (with repeated registration of sellers and buyers on marketplaces), bonus hunting and betting, cryptocurrency airdrops, web scraping, ad testing, etc.

An anti-detect browser is also useful for protecting privacy: by separating work into different profiles with unique fingerprints and separate cookies, the user prevents linking their actions across various websites (although, let’s be honest—the last thing that comes to mind when you hear “anti-detect browser” is privacy protection).

In this article, I tried to figure out how anti-detect browsers work, what technologies they use for masking, and compared popular products along with their pros and cons—and as a cherry on top for the inquisitive minds, provided some code examples for working with this marvel of privacy protection! Let’s get started!

Principles of Operation of Anti-detect Browsers (Architecture and Technologies)

An anti-detect browser, in appearance and capabilities, is almost no different from a regular browser like Chrome or Firefox—it also allows you to browse websites, install extensions, etc. The difference lies in its internal architecture: the anti-detect browser injects a layer between the website and the user’s real data, substituting the “fingerprint” on the fly. Typically, such browsers are built on the open-source code of Chromium/Firefox with modifications to the low-level components responsible for collecting system information. In other words, the developers of anti-detect solutions modify or patch the browser engine by adding special modules that intercept script requests for settings and environment data, returning fake values instead of the real ones.

It is important to understand that the fingerprint is formed from a multitude of parameters, so a simple substitution of the User-Agent or one single field is insufficient. An anti-detect browser must ensure the consistency of all provided information. To achieve this, a system of profiles is usually implemented: each profile is an isolated environment with a unique set of parameters (ranging from the OS type to the font set), maintaining its own cookies, localStorage, and cache separately. Within an anti-detect, profiles can be stored locally (in separate folders/files) or in an encrypted cloud storage on the service side.

By launching a profile, the user receives a separate browser window that websites see as a distinct device. Neither cookies nor fingerprints from another profile “leak through.” Some solutions offer hundreds or even thousands of profiles, rapid startup and switching, team access (sharing), and other management tools—in essence, a kind of browser virtualization that is simpler and cheaper than setting up hundreds of virtual machines.

Kernel-Level Masking vs. Add-ons in Anti-detect Browsers

Early attempts to combat fingerprinting were made through browser plugins (for example, in Firefox) or user JS scripts. However, such approaches are unreliable—advanced scripts can detect substitutions made at the JavaScript level (for instance, noticing that the Canvas is distorted). Modern anti-detect solutions operate deeper—at the browser kernel level. This means that when a website requests Canvas data, it receives a fake Canvas (generated with different parameters), and the canvas object along with its API appears entirely normal. Such low-level interception is harder for antifraud systems to detect. Different products implement this in their own ways. For example, one approach is the hybrid fingerprint substitution, as seen in Linken Sphere: it adjusts the profile parameters to match your actual device to minimize discrepancies (in other words, it adapts the profile to the computer rather than vice versa). Others, conversely, generate the fingerprint independently of the current device, completely isolating the profile.

There are solutions that use only real device fingerprints: for instance, Octo Browser claims that its profiles use configurations gathered from real devices, with the management of these configurations built into the browser kernel. In theory, this should make it more difficult to detect a substitution since all parameters appear quite plausible when combined.

Another technology is proprietary browser engines: for example, GoLogin, instead of Chromium, uses a fork called Orbita, designed with anonymity in mind. Some providers even allow switching the engine (Chromium or Firefox) directly in the profile settings.

Isolation and Data Security in Anti-detect Browsers

Profiles in an anti-detect browser are isolated not only functionally but also in terms of user data. A good anti-detect browser encrypts profile storage (for example, using AES encryption, as in Octo Browser) so that even developers or team members cannot read the data without permission. In team environments, it is often possible to flexibly assign access rights—from full administration to simply launching the browser with the required profile.

Many products operate cross-platform (Windows, macOS, Linux) and are not tied to specific hardware, allowing profiles to be transferred to another machine or run in the cloud. Some even let the user choose where to store profiles—locally, in the service’s cloud, or on their own server.

Thus, the architecture of an anti-detect browser is built on the principle that profiles are completely separate and plausible, while their management is centralized and secure.

Methods of Anti-detection and Fingerprint Masking

To counteract various fingerprinting methods, an anti-detect browser substitutes a whole range of environmental parameters. Here are, in my view, the key methods and approaches to masking:

IP Address and Geolocation.
The browser itself does not change your IP address—that is accomplished using a combination of proxy servers or a VPN. Anti-detect browsers typically include a proxy manager: you add a proxy for each profile (HTTP/SOCKS5), thereby substituting both the IP and the country/city of the outgoing connection. Consistency is crucial: other parameters (time zone, system language, JavaScript time zone, geolocation API) must correspond to the selected geo. Most anti-detect solutions automatically adjust these settings based on the proxy’s IP address. For example, if you use a German proxy, the profile may automatically set the time zone to GMT+1, locale to de-DE, etc. Geolocation (via the HTML5 Geolocation API) is usually either disabled or set to return the coordinates of the capital of the chosen country—or can be manually configured. Note: antifraud systems often verify both the IP and the fingerprint together, so proxies are essential for complete anonymity.
User-Agent and Client Headers.
The browser sends a User-Agent string to the server with information about itself. An anti-detect browser substitutes the appropriate User-Agent according to the selected profile (even down to the browser and OS version). However, headers alone are not sufficient—modern websites actively use Client Hints (Sec-CH-UA-* headers), which provide precise details about the OS, device model, version, etc. A good anti-detect manages these headers as well, ensuring they are consistent with the User-Agent. For example, if a profile claims to be Chrome 110 on Windows, then via Client Hints it will not reveal a real Chrome 98 running on Linux.
Canvas Fingerprint (Canvas API).
The Canvas element in HTML5 is used for covert fingerprinting: a script draws specific text or shapes and hashes the resulting image. Each computer (due to differences in GPU, drivers, fonts) produces a slightly different hash. Anti-detect browsers employ several techniques to counter this. One method is to introduce noise: they modify the Canvas context so that the final image is uniquely distorted, although these microscopic changes are imperceptible to the eye. For example, by adding a couple of “corrupted” pixels or slightly shifting the rendering. Another method is to supply websites with a pre-prepared “real” Canvas fingerprint taken from a device with a specific graphics card. This is how, for instance, MoreLogin operates, implementing its Real Canvas Fingerprint technology—it doesn’t merely generate a virtual canvas but uses a template from a real device. This increases the likelihood that the fingerprint passes plausibility checks. Typically, anti-detect browsers allow fine-tuning of Canvas parameters: you can enable or disable substitution or choose the algorithm (e.g., “noise” vs. “block”), though for most users everything works out of the box.

WebGL and GPU Information.
WebGL—an interface for 3D graphics—is also used for fingerprinting since it provides data about the video card (renderer, driver version) and similarly allows for the creation of a unique fingerprint of a 3D scene. Masking in WebGL is similar to Canvas: an anti-detect browser can emulate a different video card (by substituting GL_VENDOR and GL_RENDERER strings with values from Intel/Nvidia, etc.) and alter the rendering result through shaders. Some solutions even support substituting the video stream/WebRTC—not in terms of the camera, but regarding the graphics subsystem information—so that even in complex cases like video capture the system appears differently. The challenge is that changes to WebGL must be consistent with the overall system: you cannot, for example, claim a GPU of “Adreno” (a mobile GPU) on a desktop profile running Windows, as that would raise suspicions. Therefore, anti-detect solutions include configuration databases: sets of related parameters (“video card + driver + supported extensions”) that are applied together.
Audio Fingerprint (AudioContext).
A less obvious but also employed method is fingerprinting via the browser’s audio generator. Different systems produce subtle, almost imperceptible noise when generating an audio signal, which allows a hash of the “audio fingerprint” to be computed. An anti-detect browser may mute the audio API or modify the produced sound (for instance, by adding ultrasonic noise) to change the hash. Not all browsers give this equal attention—some popular anti-detect browsers do not manage to substitute the audio hash, which might be seen as an attempt to reduce development costs or as a belief by the developers that this aspect is not as crucial as others. In advanced products, however, this function is present.
Fonts and DOM Structure.
The list of installed fonts on a user’s system is often detected by inserting text in various fonts in a hidden manner and measuring its width (if a font is missing, the text is rendered with a fallback font and the dimensions differ). An anti-detect browser may either provide the website with a limited set of fonts (emulating that others are absent) or always return the same list. For example, some solutions do not mask fonts at all—which is considered a drawback. In addition to fonts, websites can detect the presence of popular extensions (such as AdBlock) through changes in the DOM. Typically, anti-detect browsers run without extensions except for built-in tools, thus appearing “clean” from the perspective of such checks. In some browsers, there is also an option to isolate or substitute WebRTC (so that the real local IP is not exposed) and to disable the Canvas Font DOM API (the methods that allow direct access to the list of fonts).
Hardware Identifiers.
The browser may reveal indirect data about the device: CPU architecture, number of cores, mobile device model, screen and window dimensions, presence of touch input, etc. For example, the JavaScript object navigator has properties like platform, hardwareConcurrency (number of threads), deviceMemory, maxTouchPoints, and so on. An anti-detect browser substitutes these values based on the chosen profile (e.g., 8 cores and 4GB memory for a desktop profile, or 2 cores with touch input for a mobile profile). Ideally, all these parameters are consistent with each other: if a profile is set to “Windows; x64”, it should indicate 64-bit architecture, ample memory, and no touch support; if it’s “Android”, then it should reflect a mobile GPU and touch capabilities, etc. Moreover, some anti-detect solutions claim to protect against less common methods—masking the TCP/IP stack (so that indirect network packet features do not reveal your OS), maintaining stable Canvas for WebRTC (since Canvas is used during video capture in WebRTC), etc.—but these nuances extend beyond the standard fingerprint components.
Cookies and Other Storages.
Although cookies are not part of the fingerprint, it is crucial that each profile has its own cookie database. Anti-detect browsers offer convenient functionalities—for example, a “cookie robot” that automatically bypasses popular websites to collect cookies in order to “warm up” a new profile. When importing cookies or working via an API, they ensure isolation so that cookies from one account do not mix with another. This enables, for instance, mass authorization of profiles by loading each with its own cookies from files.

By substituting all the parameters mentioned above, an anti-detect browser creates for each profile a unique and comprehensive set of data—a digital fingerprint of the device. The quality of the masking is determined by how plausible and consistent this fingerprint is. If the fingerprint contains illogical combinations (for example, claiming Windows 11 while including the “San Francisco” font from macOS, or an iPhone with a screen resolution of 1920×1080), antifraud systems might suspect deception. Therefore, the best anti-detect solutions either strictly lock related parameters (preventing the user from tampering with them) or automatically select configurations. Different products use various sets and algorithms for substitution, so fingerprints can vary—successfully passing some checks while failing others. Experience shows that the technologies are ever-evolving, leading to a constant “arms race” between anti-detect developers and fingerprinting systems.

Overview of Popular Anti-detect Browsers: Comparison, Pros and Cons

In recent years, the market for anti-detect browsers has grown significantly—by various estimates, there are over 50 solutions (and they continue to appear and evolve with varying degrees of success). However, in terms of functionality and reliability, they differ noticeably. Some are aimed at professionals with huge-scale operations, while others target beginners; some specialize in mobile fingerprints, whereas others focus on utmost simplicity. I have selected several of the most popular products and tried to examine their features, advantages, and disadvantages. (All of them implement the aforementioned fingerprint substitution methods to some degree—but differ in additional capabilities.)

Multilogin

A veteran in the anti-detect browser market. This product, developed by a European team with 10 years of experience, has become a benchmark in the industry. Multilogin offers a comprehensive “2-in-1” solution: both anti-detect browsers (Mimic based on Chromium and Stealthfox based on Firefox) and an integrated high-quality proxy service. Admittedly, I have never quite gotten used to trusting built-in “box” proxies—typically, their prices can be above market rates. I use proven residential proxies with which I have long worked.

Strengths:
– Highly reliable and quality masking; it completely substitutes all known fingerprints, including Canvas and WebGL.
– Profiles are stored in the cloud with AES encryption and support team functionalities (profile sharing, access control).
– Automation is implemented through an API and a local HTTP server for integration with Puppeteer, Selenium, Playwright (for example, a Node.js code sample for connecting Puppeteer is provided below), as well as its own built-in automation tool “ScriptRunner”.
– Positioned as a business solution—reflected in its price (starting at approximately ~$99 per month for 100 profiles).

Drawbacks:
– No free plan (only 3 trial profiles), which can be a barrier for beginners.
– Sometimes slow support and no Linux version (though I personally do not use Linux and rarely praise it).

Overall, Multilogin is a premium product: reliable, feature-rich, but expensive.

GoLogin

A popular anti-detect solution targeting a more affordable segment. It offers a desktop application, a web version, and even a mobile add-on for Android.

Core:
– Built on its proprietary browser Orbita (a Chromium fork).
– Allows fine-tuning of about 50 fingerprint parameters; profiles are stored in the cloud, and can be grouped, duplicated, and shared with a team.
– Includes a free proxy (2 units for testing) and offers a 7-day trial period.

Strengths:
– Cross-platform (Windows, Mac, Linux) with a web interface that lets you launch profiles from your browser without installing a client.
– Supports API automation (for Puppeteer, Selenium).
– Offers out-of-the-box support for mobile fingerprints on Android.
– The free plan provides 3 profiles, and paid plans are among the lowest on the market (starting at around ~$49/month for 100 profiles).

Drawbacks:
– “One-click” profiles in GoLogin are simplified and do not allow changes to some settings (OS, proxy, time zone).
– Fewer advanced team-level features.

For its price range, GoLogin delivers nearly full anti-detect functionality. It is often recommended as an affordable alternative to Multilogin, though in my personal experience I find the accessible equivalent somewhat different.

Octo Browser

A relatively new but ambitious player gaining popularity (developed in Eastern Europe). Octo emphasizes the quality of masking and speed. It uses only real device fingerprints and implements kernel-level substitutions, allowing Octo profiles to successfully pass checks by services like Pixelscan, BrowserLeaks, and Whoer.

Highlights:
– Developers update its engine very promptly—the Chromium core is updated within a few days after a new release, so Octo users aren’t left with an outdated browser.
– Unique features include human typing simulation and video spoofing.
– Offers both a one-click profile creation mode for beginners and over 50 manual settings for experts.
– Supports all mass functions (tags, grouping, profile import/export, cookie robot, etc.).

Drawbacks:
– No completely free plan (only a temporary demo access is available).
– The lower-tier packages are relatively expensive (from €29 per 10 profiles per month).
– A surge in positive online reviews may be due to aggressive marketing or a rapid increase in the user base—something to consider critically.

Technically, Octo Browser has proven itself very well and is definitely worth attention as one of the most advanced new-generation anti-detect solutions.

Dolphin{anty}

A product of Russian origin, initially developed “by arbitrageurs for arbitrageurs.” It stands out with its user-friendly interface and focus on tasks related to Facebook and other social networks. Its main attractive feature is a free plan offering 10 profiles with no time limitation, which has attracted many users, making Dolphin one of the most widespread solutions in the Russian internet (Runet).

Features:
– Provides basic functionality for free; includes a proxy and extensions manager (with unlimited extensions applied conveniently to profiles).
– Offers its own no-code automation (GUI for script scenarios in beta), integration with Selenium/Playwright/Puppeteer via API, and team work with role-based access.
– Claims to support complex resources (Google, Facebook, Coinlist, etc.), which is generally confirmed in practice.

Drawbacks:
– Currently does not implement certain subtle fingerprinting aspects (such as masking fonts, audio hash, Canvas Font DOM API, and partial DNS masking).
– There have been incidents: in 2022, around 15% of user profiles were leaked, and an update in April 2024 caused disruptions in browser performance—tarnishing its reputation somewhat.

Free because they love users, or because they sell our data?

– Personally, I dislike how Dolphin handles proxies; they often disconnect and are assigned a low level of privacy (even though I have been using those proxies for years and am the only one using them), and overall stability leaves much to be desired (though this may improve on paid plans).

AdsPower

An anti-detect solution from Southeast Asia (Singapore/China), aimed at the global market. It stands out with an abundance of automation features: besides an API, AdsPower includes a built-in RPA module (for recording and replaying actions without code) and window synchronization (mirroring user actions across multiple profiles). This enables even non-programmers to automate routine tasks—such as recording a click scenario and replicating it across hundreds of accounts.

Features:
– Supports two engines (Chromium and Firefox).
– Each profile has a unique fingerprint and is automatically configured upon creation, simplifying the setup.
– Emphasizes data security with strong encryption of storage and data transmission.
– Advantages include support for all major OS (Windows, macOS—including M1), the ability to emulate fingerprints for any platform (Windows, Mac, Linux, Android, iOS), flexible tariff customization (plans based on the number of profiles and users), a free plan (2 profiles), and a 3-day trial.

Typical Antidetect Browser from AliExpress

Drawbacks:
– The interface is often criticized for being cluttered (“tons of buttons and switches”), making it challenging for beginners.
– Translations into Russian and other languages can be awkward at times (the typical anti-detect browser from AliExpress).
– The pursuit of functionality sometimes compromises the currency of the browser kernel: updates occur roughly once a month, meaning fingerprints may lag behind the latest versions of Chrome and appear suspicious.
– No auto-update is available—new versions must be installed manually.
– AdsPower does not work on Linux.

Overall, AdsPower suits those who require maximum automation (especially for social networks and e-commerce) and are willing to accept a somewhat bulky interface for enhanced features.

AdsPower interface for a user who just wanted to change IP

Other Solutions

In addition to those mentioned above, there are other noteworthy anti-detect browsers:

Linken Sphere – A well-known product by a Russian developer. Based on Chromium, payment is made exclusively in BTC (for anonymity). It is famed for its “unbreakable” profile (employing the hybrid fingerprint substitution method described above). Reliable and functional, but it lacks a trial period and a Linux version.
Incogniton – An anti-detect browser with a free starter plan for 10 profiles. It offers excellent documentation (in both Russian and English) and a user-friendly interface. It supports the “Paste as human typing” function (emulating human typing when pasting text), mass profile creation, and integration with Selenium/Puppeteer. Paid plans start at $29.99/month and are well-suited for small businesses.
Undetectable – A young service notable for its unusual payment model. It limits the number of real configurations (device fingerprints) per account, with additional fingerprints sold for around ~$1 each. However, you can create unlimited profiles, especially locally (cloud profiles are limited). Technically, Undetectable is on par with leading solutions: regular rapid Chromium updates, three options for profile storage (local/cloud/own server), an integrated cookies bot and a generator for popular websites to automatically warm up profiles, “Paste like a human” (simulating human text pasting), a synchronizer for actions across windows, and 33 levels of access for teams. It offers a free plan (5 cloud + 10 local profiles).Drawbacks: Without purchasing additional fingerprints, profiles may repeat their “configs”; the cost of a real fingerprint is roughly ~$1 each on average; no Linux version is available.
Mobile Anti-detect Solutions. It is also worth noting solutions that emulate mobile devices. For example, GeeLark is positioned more as an “anti-detect phone”: each profile is a cloud-based Android with full smartphone capabilities (apps, video, streaming, etc.). This is ideal for TikTok, Instagram, and tasks where the mobile context is critical (such as mobile applets that cannot be opened in a desktop browser). There are also projects like Kameleo, which combine mobile and desktop profiles, though they are less known in the Russian-speaking market.
Less well-known options: Ghost Browser (more focused on multi-session work, with partial anti-detect functionality), Indigo Browser, AntBrowser, Lalicat, etc.—each with its own features, though their market share is relatively small. Several projects emerge and disappear (for instance, Brovisor and Ultimate Orb did not survive the competition).

Thus, when choosing an anti-detect solution, it is advisable to prefer time-tested products with active support.

Summary Comparison Table

For convenience, here is a comparison of the key characteristics of several reviewed solutions:

Name	Engine (Core)	OS	Free	Features	Drawbacks/Risks
Multilogin	Chromium, Firefox	Windows, macOS	Trial: 3 profiles	Most reliable fingerprint masking; cloud storage with encryption; API, scripting, proxy bank (5 GB)	Very high price; no Linux version
GoLogin	Orbita (Chromium fork)	Windows, macOS, Linux, Android (Web)	Free: 3 profiles, 7-day trial	Web version and mobile app; 50+ settings; API; significantly cheaper than Multilogin	Limitations on one-click profiles; fewer team-level functions
Octo Browser	Chromium (latest)	Windows, macOS, Linux	5-day trial (up to 100 profiles)	Real fingerprints; rapidly updated kernel; human typing simulation; video spoofing; fast, lightweight	No free plan (only temporary demo); lower-tier profiles are relatively pricey
Dolphin{anty}	Chromium	Windows, macOS, Linux	Free for 10 profiles	Free basic functionality; GUI scripting; Facebook Ads integration; role-based access	Does not mask fonts and AudioContext; data leak incidents; unclear proxy handling
AdsPower	Chromium, Firefox	Windows, macOS	Free: 2 profiles, 3-day trial	RPA macros and window synchronization; fingerprints for 5 platforms (Win/Mac/Linux/Android/iOS); flexible tariffs (customizable)	Cluttered UI; slow kernel updates (≈ once/month); no auto-update; no Linux
Undetectable	Chromium	Windows, macOS	Free: 5 (cloud) + 10 (local) profiles	Unlimited local profiles; three profile storage options; fast launch for 5000+ profiles	Without purchasing extra fingerprints, profiles may repeat; fingerprint cost ~$1 each; no Linux
Incogniton	Chromium	Windows, macOS	Free for 10 profiles	Easy to learn; excellent documentation; “Paste as human” for text input	No mobile fingerprints; small company – potential delays in support

Of course, the table does not cover all criteria (such as speed, support quality, reputation on forums, etc.), but it provides a general overview. When choosing an anti-detect solution, consider your own needs: the number of profiles required, budget, automation requirements, whether team work is planned, if mobile emulation is needed, etc. It is also useful to examine reviews (on forums or Trustpilot), keeping in mind that overly glowing reviews may be paid for, while negative reviews often arise when something isn’t working (and might not always reflect the true state of affairs but highlight certain issues). Ideally, test 2–3 options to see which best suits your tasks, as nearly all offer a free mode or trial.

A Bit More Specific Knowledge About Anti-detect Browsers

To solidify your understanding of the technical aspects, here are a few brief examples demonstrating how to work with fingerprints and automate anti-detect browsers.

1. Obtaining a Canvas Fingerprint (JS Example)

Suppose we want to see how the browser generates a Canvas fingerprint. By opening the console on any website, you can execute:

// Create a hidden canvas of 200x50 pixels
const canvas = document.createElement('canvas');
canvas.width = 200; canvas.height = 50;
const ctx = canvas.getContext('2d');

// Draw text using a specific font
ctx.textBaseline = "top";
ctx.font = "14px Arial";
ctx.fillText("Hello, world!", 2, 2);

// Retrieve the image data and hash it
const data = canvas.toDataURL();
console.log("Canvas data URI length:", data.length);

crypto.subtle.digest("SHA-256", new TextEncoder().encode(data))
  .then(buf => {
    const hashArray = Array.from(new Uint8Array(buf));
    const hashHex = hashArray.map(b => b.toString(16).padStart(2, '0')).join('');
    console.log("Canvas fingerprint SHA-256:", hashHex);
  });

In a regular browser, this will output a specific hash (for example, ab4f9e5d...), stably unique to your system. However, if you execute the same code in an anti-detect browser where the Canvas fingerprint is masked, the result will be different—and, importantly, it may change with each profile restart (if noise randomization is used). This way, you can visually verify how the anti-detect modifies the Canvas. Similarly, you can inspect values such as navigator.hardwareConcurrency, navigator.platform, or WebGLRenderingContext.getParameter(...)—the anti-detect browser will substitute the profile’s values instead of the real ones.

2. Managing a Profile via Puppeteer (Node.js)

Almost all advanced anti-detect browsers support automation through external tools (such as Puppeteer or Selenium). Consider a simplified Node.js code example for Multilogin: we open a profile via a local API and take a screenshot of a page. Suppose you already have a profile with a specific profile_id in Multilogin. Then:

const axios = require('axios');
const puppeteer = require('puppeteer-core');

const API_URL = 'http://127.0.0.1:35000';  // local Multilogin API port
const profileId = 'YOUR_PROFILE_ID';

// Step 1: Launch the profile via the local REST API
axios.get(`${API_URL}/api/v2/profile/${profileId}/start?automation_type=puppeteer`)
  .then(response => {
    const { status, data } = response;
    if (status === 200 && data.data && data.data.port) {
      const port = data.data.port;
      console.log(`Profile launched, debugger port = ${port}`);
      const browserURL = `http://127.0.0.1:${port}`;
      // Step 2: Connect Puppeteer to the launched profile
      return puppeteer.connect({ browserURL });
    } else {
      throw new Error("Failed to launch profile: " + JSON.stringify(data));
    }
  })
  .then(async browser => {
    // Step 3: Automate the browser as usual
    const page = await browser.newPage();
    await page.goto('https://www.google.com');
    await page.screenshot({ path: 'test.png' });
    await browser.close();
    console.log("Screenshot saved, profile closed.");
  })
  .catch(err => console.error("Error:", err));

This script calls the local Multilogin API to launch the specified profile (with Puppeteer automation, not in headless mode), retrieves the local port, and then connects to the browser using puppeteer.connect. After that, you can control the page as usual with Puppeteer—the script will open Google and take a screenshot. In the context of Multilogin, this occurs within the profile with all its masking functionalities. Other anti-detect solutions operate similarly—most provide either a local WebSocket/port for connection or allow launching via command line with a specified Debugger Address.

3. Correspondence Table of Fingerprinting Methods and Countermeasures

To summarize, here is a condensed table showing which fingerprinting techniques are employed and how the anti-detect browser “breaks” them:

Method of Identification	What the Website Does	How the Anti-detect Masks It
Canvas Fingerprint	Draws an image on a <canvas> and hashes its pixels. The unique hash serves as the device ID.	Distorts the rendering (adds noise) or provides a canvas from another GPU (Real Canvas), thus changing the hash to a preset or random value.
WebGL Fingerprint	Renders a 3D scene via WebGL, reading properties like UNMASKED_VENDOR/RENDERER and capabilities.	Substitutes the GPU and driver names with other values; alters the rendering result (by adding noise in shaders) to remain consistent with the declared GPU.
AudioContext Fingerprint	Creates an audio context, generates a sound (using an oscillator), and computes a hash of the audio sample. Different sound cards produce different hashes.	Adds subtle noise to the audio output or fixes audio parameters, potentially returning a uniform “reference” sound so that the fingerprint is no longer unique. (Some anti-detects have not fully implemented this yet.)
Font Enumeration (DOM)	Iterates through known fonts (by measuring text dimensions) to determine which fonts are installed—each system has a unique set.	Limits the visible font list (e.g., only default system fonts) or always returns the same set. It can emulate fonts from different OSes based on the profile.
Navigator/UA Hints	Reads properties from the navigator object (such as userAgent, platform, deviceMemory, languages, CPU threads, plugins, MIME types, touch support, Battery API, etc.).	Sets these properties to match the profile configuration (down to fine details). For example, setting navigator.platform to "Win32" and hardwareConcurrency to 8 for a Windows profile; maxTouchPoints to 5 for an Android profile. Battery and sensor information are often disabled (indicating non-support).
Client Hints (Sec-CH-UA)	Requests additional headers from the browser, such as device model (Sec-CH-UA-Model), OS version (Sec-CH-UA-Platform-Version), etc.	The anti-detect browser generates these headers according to the profile. For instance, if the profile is “Windows 10, Chrome 108”, then Sec-CH-UA-Platform will return “Windows” and Sec-CH-UA-Platform-Version will return “10.0”.
WebRTC Leak (ICE)	Attempts to obtain the local IP address via WebRTC ICE (using, for example, RTCPeerConnection and candidate strings).	Substitutes the local IP with a fake one (usually 0.0.0.0 or within a proxy range) or completely disables WebRTC when masking is enabled.
Persistence Identification	Uses supercookies such as Evercookie, IndexedDB, localStorage, Service Workers to “mark” the browser persistently.	Isolates storage per profile, preventing data from one profile from being linked with another. Additionally, when a profile is cleared, all such data is removed. Some anti-detects allow rapid cloning of a clean profile, thereby avoiding “long-lived” identifiers.

This table provides a general overview of the mechanisms; specific implementations may vary, but overall it illustrates how comprehensively an anti-detect browser falsifies the user’s environment.

Unique Aspects and Additional Tips – Delving Even Deeper into the World of Anti-detect Browsers

Consistency of the Entire Network Stack.
Even with perfect substitution at the browser level, sometimes lower-level indicators—such as peculiarities of the operating system’s TCP/IP stack (e.g., TCP window size, packet ordering, etc.)—can reveal the user. Based on such clues, major companies could theoretically determine that multiple fingerprints are actually coming from a single OS. Some anti-detects (like Multilogin X) claim to have measures against this, though the specific methods are not disclosed. In critical cases, it is advisable to use different virtual machines or even different operating systems (for example, running some profiles on Windows and others on Linux) to mitigate this vulnerability.

Automation and Anti-detect: Best Practices.
If you plan to automate actions on a large scale using an anti-detect (for botting, scraping, automated tests, etc.), note that aside from the browser itself, you must simulate human behavior. Simply having different fingerprints won’t suffice if a bot visits 100 times a minute and clicks in the same pattern. At a minimum, use built-in tools such as AdsPower’s RPA for recording human-like scripts, or functions like “imitate human” (for example, Octo and Multilogin include human typing simulation with delays and random errors). You can also integrate libraries like puppeteer-extra-plugin-stealth, although in the case of anti-detect browsers they are often redundant. If you need a clicker for tens of thousands of accounts (especially in games or social networks), consider not only browsers but also mobile emulators and other approaches, as hardware and network limitations can become the bottleneck.

Considering the Human Factor.
If you work in a team or delegate routine tasks to employees, an anti-detect browser allows for access separation, but it also requires organizational measures. For example, when working in different time zones, profiles should be set to the proxy’s time zone rather than the employee’s actual location to avoid desynchronization. Also, do not overlook operational security: using an anti-detect, it is easy to get carried away with its capabilities and, for example, accidentally log into your primary Google account within a profile that has a mismatched fingerprint via someone else’s proxy. This could lead to account compromise. Always separate work profiles from personal ones and maintain good practices (e.g., don’t accidentally paste your password into the wrong field).

Anti-detect browsers are powerful tools, but they must be used with care.

Conclusion

Anti-detect browsers are complex yet extremely useful tools in the era of pervasive online tracking. I have attempted to explore the technical aspects of how they operate—from their architecture and primary masking technologies to an overview of popular solutions and their features. The key takeaway is that there is no “magic invisibility”; rather, there is painstaking imitation of numerous parameters, which, when used correctly, offers a genuinely high level of anonymity and flexibility. For advanced users, anti-detect solutions open up opportunities that, just a few years ago, required owning a fleet of computers or virtual machines. Today, simply launching a specialized browser provides you with dozens of “identities” tailored to your needs.

Anti-detect Browsers — How They Work, Which Anti-detect Browser to Choose, Personal Experience, and a Bit of Code