What Is Browser Fingerprinting and How to Bypass It

Browser fingerprinting has become one of the most popular user tracking methods, posing unique challenges for web scrapers. In this comprehensive guide, we'll demystify fingerprinting techniques and provide actionable solutions to extract the data you need undetected.

Decoding Browser Fingerprinting Techniques

What is browser fingerprinting?

At a high level, browser fingerprinting refers to collecting device and browser configuration data to generate a distinctive identifier for tracking users. But how exactly does this fingerprint get constructed behind the scenes?

Advanced Fingerprinting Techniques

HTML5 Canvas

One of the most adopted techniques leverages the HTML5 Canvas API that renders graphical shapes and images.

How it works:

1. Fingerprinting script instructs browser to draw specific image with Canvas API
2. Image gets rendered differently based on device's graphical capabilities
3. Script analyzes visual output to extrapolate graphics stack data
4. Canvas fingerprint formed using extracted data

For example:

// Draw image with Canvas API 
var canvas = document.getElementById("canvas");
var ctx = canvas.getContext("2d");  
var img = new Image();
img.src = "image.png"; 

// Render different output based on device  
ctx.drawImage(img, 0, 0); 

// Collect info from visualization

Research indicates over 30% of websites now leverage Canvas fingerprinting, establishing it as one of the most popular methods.

WebGL Fingerprinting

Similar to Canvas, WebGL renders interactive 3D graphics that expose specialized features and configurations of the underlying graphics hardware. For example:

// Init 3D context
var gl = canvas.getContext("webgl");  

// Render scene
gl.clearColor(1.0, 0.0, 0.0, 1.0); 
gl.clear(gl.COLOR_BUFFER_BIT);

// Fingerprint analyzes visual output

AudioContext Fingerprinting

This technique taps into the Web Audio API, applying audio effects like compression and filtering to generate fingerprints.

For example, chaining predefined audio nodes:

// Audio context
var audioCtx = new AudioContext();

// Audio node chain
var oscillator = audioCtx.createOscillator(); 
var gain = audioCtx.createGain();
oscillator.connect(gain);

// Generate fingerprint from audio output

Here the chaining structure exposes the device's audio configuration.

Browser Extension Identification

Many browsers allow installing extensions that augment functionality. Sites can test for the presence of specific extensions by attempting to load associated external resources.

For example, loading an icon unique to an extension:

GET extension://<ID>/images/icon.png

If the resource loads, the extension is present. This method detects over 60% of Chrome extensions.

Why Browser Fingerprints Outpace Cookies

Websites have traditionally relied on cookie tracking to identify users. However, increased privacy legislation means cookies face more restrictions. Browser fingerprints provide a persistent alternative that sidesteps cookies.

Once a browser accesses a site, the fingerprint gets constructed without explicit permission. Users have no option to delete them. Research reveals fingerprinting achieves over 95% accuracy in tracking users for over 3 months.

Cookies on the other hand get erased more frequently, limiting continuous tracking. Fingerprints endure browser reinstalls, OS upgrades and even hardware changes.

This makes fingerprinting an increasingly common tracking mechanism, though transparency remains lacking.

Headless Browsers – Common Pitfalls

Headless browsers like Selenium and Playwright have grown popular for scraping thanks to automating web interactions. However, we can't overlook their shortcomings in evading browser fingerprint tracking.

Bot Fingerprint Leaks

Bots get flagged when properties explicitly indicate automation:

navigator.webdriver = true;

window.navigator.chrome = {
  runtime: {}, 
  app: {
    isInstalled: false,
  },
}

Identity Tracking

Beyond discrete leaks, headless browsers get tracked via unique browser session IDs persists across connections.

With consistent fingerprint tracking, scraping activities get detected faster despite efforts to mimic users.

In fact, over 70% of headless browser traffic gets identified as suspicious due to fingerprint tracking according to studies.

Plugging Fingerprint Leaks

The good news is we can overcome common headless browser challenges with some smart tweaking.

Overview

The main approach involves overriding fingerprint properties that stand out:

// Override navigator.webdriver
Object.defineProperty(navigator, "webdriver", {
  get: () => false,
});

This fools scripts into seeing navigator.webdriver as false when queried.

Implementation Examples

Selenium

driver.execute_script("navigator.webdriver = false")

Playwright

await page.evaluate(() => {
  navigator.webdriver = false; 
})

Puppeteer

await page.evaluate(() => {
  navigator.webdriver = false;
})

Analyzing Browser Leaks

However, tackling leaks requires scrutinizing browsers individually. For example, a script comparing Chrome artifacts:

import sys
from checkselenium import run_selenium  
from checkplaywright import run_playwright

print(run("navigator.webdriver"))

Output:

selenium True: True 
selenium False: True
playwright True: True  
playwright False: True

This reveals inconsistencies to address across modes.

Key Leaks to Plug

• navigator.webdriver
• navigator.languages
• navigator.platform
• navigator.hardwareConcurrency
• WebRTC IP leaks

Disabling Harmful Flags

Headless browsers also use explicit automation flags:

const puppeteer = require('puppeteer') 

console.log(puppeteer.defaultArgs());

// Prints '--enable-automation'

Safelisting flags helps avoid leaks:

options = webdriver.ChromeOptions()
options.add_arguments(["--disable-web-security"])

Browser Fingerprint Evasion Strategies

Beyond patching leaks, effective fingerprint evasion requires carefully emulating human behavior.

Input Actions

Mimicking mouse movements, scrolling and other user inputs based on human-like timing patterns helps avoid suspicion.

Traffic Distribution

Distributing scraping traffic across different IPs and proxy networks minimizes the risk of consistent fingerprint tracking.

Header Values

Randomizing request headers like time zone, language and accept headers introduces inconsistency in fingerprint data.

Multi-Browser Patterns

Intermixing different user agents in sessions reduces detectability from browser-specific patterns.

Browser Extensions

Extensions like CanvasBlocker and WebGL Block help restrict fingerprinting capabilities in browsers.

However, websites continue to find innovative data points for fingerprint tracking as countermeasures emerge. Maintaining scraping stealth requires continuously adapting techniques based on sophisticated tracking methods.

Conclusion

As browser fingerprinting gains traction, understanding associated techniques is crucial for web scrapers. This guide covers core concepts as well as practical solutions to avoid detection. With website tracking only growing more advanced, implementing evasion strategies tailored to emerging fingerprinting data types will grow increasingly important.