7 best browser fingerprinting evasion techniques

Websites are becoming more sophisticated at detecting and blocking automated traffic. In particular, they now use a technique called browser fingerprinting.

Browser fingerprinting is a Javascript-based method of gathering small bits of data about your browser, generating a unique hash value that acts as a "fingerprint". Websites then use your “fingerprint” to track your activities.

Browser fingerprinting is much harder to detect and avoid than traditional cookie- and IP-based blocking techniques. However, there are ways to evade it through spoofing. 

In this article we’ll list the most effective tools and techniques available in 2025 to spoof browser and device fingerprints. 

By using the strategies outlined here, you can better navigate defenses and maintain scraping operations at scale.

The role of browser fingerprinting in website security

Companies face evolving threats involving DDOS attacks, brute force logins, problematic purchasing, and other kinds of illicit or malicious acts. It only makes sense that website developers create evolving, robust defenses.

That's why websites now rely heavily on browser fingerprinting as a core part of their defenses.

By collecting a wide range of data points from a user's browser and device, a website can generate a unique fingerprint that’s extremely difficult for bots to fake or rotate. 

Once a scraper's fingerprint is flagged as suspicious or identified as non-human, the website may restrict its access or completely block it. 

Typical fingerprinting data includes: 

• Browser version
• Operating system 
• Screen size and resolution
• Installed fonts and plugins 
• Timezone 
• Language settings
• WebGL and canvas rendering data 
• Hardware specifications like CPU and GPU details

Detection models are also starting to factor in behavioral signals like mouse movements and keyboard interactions.

To successfully pass through these defenses, web scrapers must go beyond simply rotating IP addresses or user-agent strings. 

They need to accurately mimic legitimate browser fingerprints and replicate normal user behavior patterns. Without an effective fingerprint management strategy, even well-disguised scraping operations will eventually get detected and shut down.

Why fingerprint evasion is important for successful web scraping in 2025

As websites deploy more advanced defenses, web scrapers need to evolve with them. What keeps out malicious actors may also block legitimate web scraping activity.

Any serious web scraping strategy needs effective fingerprint evasion to maintain long-term, large scraping operations.

Avoiding detection and blocking

Detection systems today use machine learning models that recognize patterns unique to bots, making basic tricks like rotating user agents or proxies insufficient on their own.

Without robust fingerprint evasion, scrapers risk getting flagged almost instantly. Once a scraping bot is detected, it faces IP bans, forced CAPTCHA challenges, or complete blacklisting from the website. 

In many cases, detection systems retain historical fingerprint data across multiple sessions, making recovery from these blocks progressively harder.

Spoofing browser and device fingerprints accurately is the best way to prevent these initial detection triggers. 

By replicating human browsing environments down to the smallest detail, scrapers can operate for longer periods without raising red flags, significantly increasing their chances of maintaining access to target websites.

Maintaining data collection continuity

Data scraping projects often rely on consistent, uninterrupted data flows to fuel business processes like price monitoring, market analysis, or product tracking. Interruptions caused by detection events can create gaps in datasets, leading to inaccurate insights, missed opportunities, and poor decision-making downstream.

When scrapers get blocked mid-collection due to fingerprint mismatches, they often need to restart sessions, rotate credentials, or even reengineer scraping workflows. 

These disruptions add operational overhead and delay the time to data delivery, especially when scaling to dozens or hundreds of target sites.

Proper fingerprint evasion ensures that scrapers maintain stable, continuous sessions over extended periods. This stability reduces error rates, minimizes downtime, and supports high-quality, real-time data gathering— a critical requirement for teams that rely on scraping as part of their daily operations.

Scaling scraping operations

Scaling a scraping operation means going from hundreds of requests per day to tens of thousands or even millions. 

Websites deploy aggressive rate-limiting and behavior analysis systems that correlate requests not just by IP address but by fingerprint similarities. Without advanced fingerprint rotation and evasion, scaling quickly becomes a liability.

Simple proxy rotation is no longer enough to evade detection thresholds at scale. When fingerprints stay static or show obvious anomalies, detection models identify and cluster them, leading to broader bans across entire proxy pools. 

This drastically reduces the efficiency of large-scale scraping efforts.

To achieve true scale, scrapers must emulate a wide range of realistic browser fingerprints and distribute them intelligently across requests. Managing diverse, high-fidelity fingerprints allows scrapers to operate at volume without drawing attention, enabling sustained, large-scale data extraction campaigns.

Accessing data behind advanced security measures

Many websites protecting sensitive or valuable data have moved beyond traditional bot mitigation strategies. 

They now combine fingerprinting, behavioral biometrics, and real-time user validation techniques to secure their content. Without strong fingerprint evasion, scraping these targets becomes nearly impossible.

These systems are trained to recognize not just technical inconsistencies but also behavioral deviations. 

For example, an odd screen resolution paired with abnormal scrolling behavior might instantly classify a session as a bot, even if the request headers appear legitimate. Fingerprinting is a major part of this deeper profiling.

Advanced fingerprint spoofing— combined with behavioral emulation— allows scrapers to pass through these defenses. By building realistic browser environments and mimicking real-world user actions, scrapers can access protected data streams that are otherwise hidden behind multi-layered bot detection systems.

Top fingerprint evasion tools and techniques for web scraping in 2025

You need a combination of specialized tools and advanced techniques to manage and spoof fingerprints effectively. Choosing the right approach is critical for bypassing detection and maintaining scraping reliability at scale.

Stealth browsers (e.g., puppeteer-extra-plugin-stealth, selenium stealth)

Stealth browsers are modified browser automation frameworks designed to minimize detectable differences between headless browsers and real user sessions. Plugins like puppeteer-extra-plugin-stealth for Puppeteer or selenium-stealth for Selenium automatically patch known fingerprint leaks, such as navigator.webdriver, WebGL metadata, and font rendering anomalies.

To implement, install the plugin alongside your automation framework and enable the necessary stealth settings before launching browser sessions. For example, with Puppeteer, you can add puppeteer-extra-plugin-stealth to your project and configure it to handle stealth modifications automatically.

Stealth browsers let you pass through basic and intermediate fingerprint checks without needing to manually patch each fingerprint vector. However, you need to keep them updated, as anti-bot vendors continually discover new detection vectors.

Rotating real device fingerprints (e.g., fingerprint switchers, mobile farms)

Rotating real device fingerprints involves using pools of genuine browser and device configurations to simulate natural diversity. Some scrapers build mobile farms or use services that offer access to thousands of unique, real-world device profiles, covering variations in hardware, operating systems, and browsers.

To integrate this approach, you can either manually configure device settings for each session or leverage third-party fingerprint switcher APIs that automate rotation. When setting up a custom farm, you can script tools like Appium or headless emulators to randomize device parameters dynamically.

This method offers high success rates against fingerprint-based detection because the fingerprints come from real hardware and software configurations. However, it needs careful management to avoid overusing the same fingerprints, which can still raise suspicion over time.

Canvas and WebGL spoofing

Canvas and WebGL spoofing focuses on modifying how a browser renders graphic content, which is a fingerprinting target. Many websites generate a unique hash based on a device’s canvas rendering output. That means scrapers need to spoof or randomize this data.

To implement, you can use Puppeteer or Selenium plugins that intercept and modify canvas API calls. Some advanced setups modify the browser’s underlying rendering stack or inject custom JavaScript into pages to spoof canvas and WebGL outputs on the fly.

Spoofing these elements makes it significantly harder for websites to track scrapers based on rendering profiles. The main consideration is balancing randomness with realism— too much variation can itself become a signal of non-human behavior.

Browser automation on residential proxies with fingerprint management

Combining browser automation with residential proxies and strong fingerprint management creates a highly resilient scraping setup. Residential proxies– like the ones SOAX use– provide IP addresses tied to real households, while fingerprint management makes sure that each session appears unique and authentic.

To set this up, you need to integrate proxy rotation with tools like Puppeteer or Playwright, and couple that with dynamic fingerprint injection at session start. Some commercial proxy services also offer fingerprint APIs to synchronize IPs and browser profiles automatically.

This layered approach dramatically lowers detection risks by covering both network-level and browser-level defenses. However, it can be more expensive and operationally complex compared to simpler scraping architectures.

Using dedicated fingerprinting APIs (e.g., Multilogin, Kameleo)

Dedicated fingerprinting APIs like Multilogin or Kameleo offer virtual browser environments where you can precisely control and rotate all major fingerprinting parameters. 

These platforms provide ready-to-use profiles optimized for scraping and automated scaling.

Integrating these APIs involves using their SDKs or browser automation tools to launch sessions with randomized, human-like fingerprints. They allow for deep customization across parameters like timezone, screen size, WebGL data, and even device motion sensors.

The major advantage is convenience and scalability— scrapers can launch thousands of unique, undetectable sessions with minimal configuration. 

The tradeoff is dependency on third-party services and potential vendor lock-in if the service changes pricing or policies.

Advanced headless browser orchestration (e.g., Playwright with stealth mode)

Advanced orchestration tools like Playwright with stealth configurations allow scrapers to fine-tune every aspect of browser behavior. 

Playwright supports full control over browser contexts, user agent strings, viewport sizes, geolocation, permissions, and more, making it possible to simulate highly realistic browsing environments.

To implement this, set up Playwright with stealth patches or extensions that handle fingerprint masking. 

Configure each browser context with randomized but plausible settings, and rotate contexts between scraping tasks to minimize fingerprint reuse. Playwright’s multi-browser support (Chromium, Firefox, WebKit) adds another layer of diversity.

This technique provides unmatched flexibility for scrapers targeting complex sites. The tradeoff is that it needs careful scripting and session management to avoid subtle mistakes that can still expose automation behavior to detection systems.

Behavioral simulation and interaction scripting

Beyond technical fingerprints, many modern websites analyze user behavior patterns— such as mouse movements, click timing, and scrolling behavior— to detect bots. Behavioral simulation involves scripting natural user interactions into the scraping flow to appear more human.

To use this approach, integrate libraries or custom scripts that perform slight, randomized mouse movements, keyboard inputs, page scrolls, and pauses. Tools like GhostCursor or custom Playwright scripts can automate these human-like actions during scraping sessions.

Behavioral simulation strengthens fingerprint evasion by making automated browsers act like real users, blending both technical and behavioral indicators. The key is to keep actions subtle and realistic, as exaggerated or repetitive behavior can still raise flags.

Implementing fingerprint evasion in your scraping workflow

Here’s a coded PoC on how you can implement fingerprint evasion while scraping at scale.

It includes the techniques we’ve discussed:

• Proxy rotation
• Fingerprint rotation
• Browser context management
• Human-like behavior simulation
• Error handling and retries
• Dynamic waiting between proxy rotations
• Cookie management
• Scalable scraping

 const { chromium } = require('playwright-extra');
const StealthPlugin = require('playwright-extra-plugin-stealth')();
const fs = require('fs');
const path = require('path');

// Add stealth mode
chromium.use(StealthPlugin);

// SOAX proxy pool (you can generate dynamic lists or pull from API)
const proxies = [
  { host: 'proxy.soax.com', port: '9000', username: 'your-user-1', password: 'your-pass-1' },
  { host: 'proxy.soax.com', port: '9000', username: 'your-user-2', password: 'your-pass-2' },
  // Add more proxies here if needed
];

// User agents pool
const userAgents = [
  'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36',
  'Mozilla/5.0 (Macintosh; Intel Mac OS X 11_4) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.0 Safari/605.1.15',
  'Mozilla/5.0 (Linux; Android 11; Pixel 5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Mobile Safari/537.36',
];

const timezones = ['America/New_York', 'Europe/London', 'Asia/Tokyo'];

const randomItem = (arr) => arr[Math.floor(Math.random() * arr.length)];
const delay = (ms) => new Promise(res => setTimeout(res, ms));

async function scrapeWithRotation(targetUrl) {
  for (const proxy of proxies) {
    const userAgent = randomItem(userAgents);
    const viewport = { width: 1200 + Math.floor(Math.random() * 300), height: 700 + Math.floor(Math.random() * 300) };
    const timezoneId = randomItem(timezones);

    console.log(`\nStarting new session:`);
    console.log(`Using Proxy: ${proxy.username}@${proxy.host}:${proxy.port}`);
    console.log(`Using User Agent: ${userAgent}`);
    console.log(`Using Timezone: ${timezoneId}`);

    const browser = await chromium.launch({
      headless: false,
      proxy: {
        server: `http://${proxy.host}:${proxy.port}`,
        username: proxy.username,
        password: proxy.password,
      },
    });

    const context = await browser.newContext({
      userAgent,
      viewport,
      timezoneId,
      locale: 'en-US',
    });

    const page = await context.newPage();

    const cookieFile = path.join(__dirname, `cookies-${proxy.username}.json`);

    // Load cookies if available
    if (fs.existsSync(cookieFile)) {
      const cookies = JSON.parse(fs.readFileSync(cookieFile, 'utf8'));
      await context.addCookies(cookies);
      console.log('Cookies loaded.');
    }

    try {
      await page.goto(targetUrl, { waitUntil: 'networkidle' });

      // Simulate human behavior
      await delay(2000 + Math.random() * 3000);
      await page.mouse.move(100 + Math.random() * 400, 100 + Math.random() * 300);
      await delay(1000 + Math.random() * 2000);
      await page.mouse.wheel(0, 600);
      await delay(1500 + Math.random() * 3000);

      // Scrape something
      const title = await page.title();
      console.log('Scraped Title:', title);

      // Save updated cookies
      const cookies = await context.cookies();
      fs.writeFileSync(cookieFile, JSON.stringify(cookies, null, 2));
      console.log('Cookies saved.');
    } catch (error) {
      console.error('Scraping error:', error);
    }

    await browser.close();

    console.log('Session completed. Waiting before next rotation...');
    await delay(5000 + Math.random() * 5000); // Wait before next proxy
  }

  console.log('\nAll proxies rotated. Scraping cycle complete.');
}

// Usage
(async () => {
  await scrapeWithRotation('https://example.com');
})();

Challenges and best practices for undetectable scraping

Web scraping has become an essential tool for gathering data from websites. As websites defense techniques evolve, so must web-scraping strategies. 

In this section, we'll explore the key challenges in undetectable scraping and the best practices that can help you navigate this ever-changing landscape.

Website-specific anti-scraping measures

Different websites employ distinct anti-scraping techniques, making one-size-fits-all solutions ineffective. 

Some websites use rate limiting to control the number of requests a user can make in a given period. To avoid hitting these rate limits, you must rotate proxies, use dynamic delays between requests, and manage request frequency carefully.

Many websites also employ CAPTCHA systems to verify that a user is human. Dealing with CAPTCHAs typically involves using anti-captcha services or headless browsers like Playwright or Puppeteer to solve them automatically. However, these solutions can add complexity and extra costs.

Websites that rely heavily on JavaScript for dynamically loading content present another challenge. To scrape these sites effectively, you need to use tools that support JavaScript rendering, such as headless browsers like Playwright or Puppeteer. Without these tools, a scraper might fail to capture all the data.

Each of these measures needs a tailored evasion strategy. A deep understanding of the techniques employed by the target website is the key to successfully bypassing them.

The arms race between scrapers and anti-scraping

Web scraping and anti-scraping methods are in a constant arms race. 

Many websites have implemented advanced bot detection mechanisms, often using machine learning and AI to analyze traffic patterns, user behavior, and request anomalies. 

In response, scrapers must rotate fingerprints, simulate human-like browsing, and carefully manage proxy strategies.

This ongoing evolution means that both website defense systems and scraper solutions will continue to improve. 

Ethical considerations

While scraping can be an invaluable tool, it’s important to approach it with ethics in mind. One of the first ethical guidelines is to respect the robots.txt file. 

Websites use robots.txt to indicate which parts of the site can be crawled. Although it's possible to ignore these rules, it's best to respect them to avoid potential legal issues and to avoid unnecessarily placing a load on the website’s infrastructure.

Another important ethical guideline is to avoid excessive scraping. Scraping too frequently or aggressively can harm a website by putting too much strain on its resources. 

It’s important to implement rate limiting on your end and avoid scraping large sites continuously without breaks. This keeps your scraping sustainable and doesn’t impact the performance of the target website.

Be mindful of how your actions impact the target website. Don’t cause unnecessary harm or disruptions.

Monitoring and adaptation

Even with the best evasion techniques, scraping needs continuous monitoring and adaptation. You should regularly track success rates to identify when a website has updated its defense measures. This allows you to adjust your approach quickly before getting blocked.

Regular updates to tools and strategies will keep you effective over the long term.

What works today might not work tomorrow, so staying ahead of the game is essential.

The future of anti-bot measures and browser fingerprinting

As websites continue to refine their defense measures, scrapers need to adopt advanced tools and techniques to continue to do their job. 

Without evasion techniques, your scraping efforts are likely to face roadblocks, resulting in interruptions to data collection and potential loss of valuable insights. 

If you’re a developer seeking to enhance your scraping workflows, be sure to check out Web Unblocker, which automatically manages your proxies, headers, cookies, fingerprinting, and headless browsers to bypass even the most sophisticated anti-bot systems, CAPTCHAs, and WAFs, and return fully rendered pages.