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What Is Font Fingerprinting?

Emily Chen
Emily Chen

Advanced Data Extraction Specialist

14-Nov-2024

Font fingerprinting is an advanced technique used for online tracking, utilizing the unique set of fonts installed on a user's device. It is a type of device fingerprinting, similar to WebGL or Canvas fingerprinting, but instead of relying on graphics rendering or web elements, font fingerprinting focuses on the fonts available and how they are rendered by the browser. This technique is employed by websites to gather information about the devices, and in turn, the users, without requiring explicit consent or the use of traditional tracking methods like cookies.

In this article, we will explore what font fingerprinting is, how it works, its applications, and the potential privacy risks associated with it. We will also discuss how to prevent font fingerprint leakage and how web scrapers can manage font settings to prevent detection.

How Does Font Fingerprinting Work?

Font fingerprinting works by leveraging the fact that every device has a unique combination of installed fonts. These fonts are used by the operating system and browser to render text on websites. However, not all devices have the same fonts installed. Operating systems, regional preferences, and even user preferences contribute to the variation in fonts. Websites can detect this variation by querying the browser and comparing the fonts used on a webpage.

Here’s a step-by-step breakdown of how font fingerprinting works:

  1. Font Detection: When a user visits a website, JavaScript embedded on the page runs a script that checks which fonts are available on the user’s device. The website will typically create a hidden element (like a div or canvas) and attempt to render text using different fonts. It will check whether specific fonts are installed by comparing the width and rendering style of the text.

  2. Collecting Data: The script checks for common fonts (such as Arial, Times New Roman, or Courier) and also less commonly used fonts. It may try to detect more obscure fonts that are installed based on specific operating systems or regional language settings. The website may use these results to create a profile of the user’s device.

  3. Creating the Fingerprint: Based on the fonts detected, a unique identifier, or "fingerprint," is generated. This identifier can be persistent and used to track the user across multiple visits and websites. The fingerprint is often a combination of factors, such as the fonts detected and how the text is rendered.

  4. Tracking Users: Once the fingerprint is created, it can be stored in a database or a cookie and used to track the user over time. Even if the user clears their cookies or switches browsers, their font fingerprint may still be identifiable, allowing websites to continue tracking their activity.

Applications of Font Fingerprinting

Font fingerprinting has a wide range of applications, both for legitimate purposes and for potentially intrusive activities like user tracking. Here are some of the key areas where font fingerprinting is used:

Application Description Example Use Case
Ad Targeting Font fingerprinting helps advertisers create more detailed user profiles for targeted ads. Advertisers track users across different websites to serve personalized ads based on their font fingerprint.
Analytics Used by website owners to analyze traffic and improve user experience by understanding device characteristics. Website owners track users based on their device’s font fingerprint for better targeting and user experience optimization.
Cross-Site Tracking Tracks users across different websites by collecting font data and linking it to a persistent identifier. Data brokers and advertisers track users’ activity across websites without cookies, using font fingerprints.
Fraud Prevention Identifies suspicious activity by comparing device characteristics and flagging anomalies. Online banking systems detect fraudulent activity based on unusual font fingerprints linked to malicious actors.
Device Profiling Helps identify users by profiling their hardware and software setups based on installed fonts. Companies use font fingerprints to track devices used by customers for targeted campaigns or fraud prevention.
User Behavior Analysis Understands user behavior by analyzing device features and fonts. Web developers track users’ preferences for better content customization based on their font fingerprint.

Font Fingerprinting Techniques

Font fingerprinting is a technique employed by websites to gather information about the fonts installed on your device. This process involves executing scripts in the background that collect data on what fonts the browser can display. Let's dive deeper into the specific methods websites use for font fingerprinting.

1. Font Enumeration

Font enumeration is one of the simplest and most commonly used methods of font fingerprinting. This technique involves using JavaScript to check for the fonts that are available on the user’s system.

Here’s how it works:

  • When a user visits a website, the website’s code runs in the browser and triggers the font enumeration process. This is typically done by calling JavaScript functions that access the FontFaceSet interface or similar methods available in modern browsers.

  • Once initiated, the browser responds by providing a list of fonts it can render. This information is crucial for creating the fingerprint.

  • The website collects and processes the font data, often combining it with other fingerprinting techniques like canvas fingerprinting or TLS fingerprinting. The types of data collected may include:

    • Font Family, such as "Helvetica"
    • Font Name, like "Helvetica Oblique"
    • PostScript Name, for example, "HelveticaOblique"
    • Style, such as "Regular"
    • Font Sizes
  • After collecting this data, the website analyzes it to generate a unique fingerprint. This fingerprint can be based on the specific combination of fonts installed on the system, their order, and sometimes the subtle ways in which the fonts are rendered.

Learn more about FontFaceSet to understand the underlying APIs involved.

2. Font Detection

Font detection is a more advanced technique used in font fingerprinting. Unlike font enumeration, which directly asks the browser for a list of installed fonts, font detection tests whether specific fonts are installed by rendering text with different fonts.

Here’s how it works:

  • The website triggers font detection by instructing the browser to display a paragraph of text using a particular font.

  • After the text is rendered, the website measures the size of the text, calculating both the width and height of the text element.

  • The rendered text size is then compared with a reference size. If the sizes match, it suggests that the font is installed on the user’s system.

  • This method may involve testing various fonts or different versions of the same font, providing valuable data about the fonts present on the system.

Font detection is often used in conjunction with other fingerprinting techniques to gather more comprehensive information about the user’s system.

3. Canvas-Font Fingerprinting

Canvas-font fingerprinting is a more sophisticated technique and one of the most widely used methods for tracking users online. This method generates a highly unique identifier based on the way fonts are rendered in a hidden HTML canvas element.

Here’s how it works:

  • The website instructs the browser to draw text onto a hidden canvas element using a specific font. This is done behind the scenes and doesn’t affect what the user sees.

  • After the text is rendered, the website extracts the pixel data from the canvas, which represents how the text looks on the screen.

  • The pixel data is then hashed using an algorithm like SHA-256, producing a unique fingerprint for that font rendering.

  • This fingerprint is used to track and identify the user across different sessions and websites. The generated hash serves as a persistent identifier, even if the user clears their cookies.

The text used for rendering typically includes all the letters of the alphabet, called a pangram. For instance, the sentence "Cwm fjordbank glyphs vext quiz" includes every letter of the alphabet. However, the exact text may vary depending on the website’s scripts.

Explore how canvas fingerprinting works to learn about its wide usage and implications in tracking.

Canvas-font fingerprinting is especially effective because the rendering behavior varies based on factors like the user’s operating system, browser, and graphics hardware, making it extremely difficult to block or spoof.

Summary of Font Fingerprinting Methods

Technique Description Purpose
Font Enumeration Directly queries the browser for a list of installed fonts using JavaScript. To gather a unique set of fonts available on the user’s device.
Font Detection Renders text with a specific font and measures the size of the rendered text to check if the font is installed. To indirectly detect fonts by testing how they render text.
Canvas-Font Fingerprinting Uses hidden canvas elements to render text and hashes the pixel data into a unique identifier. To generate a highly unique fingerprint based on font rendering.

The Security Risks of Font Fingerprinting

Font fingerprinting raises significant privacy and security concerns. Some of the risks include:

  1. Persistent Tracking: Font fingerprints, unlike cookies, are not easily deleted. Once a fingerprint is generated, it can be used to track the user across multiple sessions and websites, even if they clear their cookies or use incognito mode. This makes it difficult for users to maintain anonymity online.

  2. Cross-Site Tracking: Because font fingerprinting works across different websites, it can create a more detailed and comprehensive profile of a user. Data brokers and advertisers can combine font fingerprinting with other tracking methods to monitor a user’s online activity across multiple domains.

  3. Device Profiling: Font fingerprints can reveal specific details about a user’s device, including the operating system, language settings, and installed fonts. This information could be used to profile users for targeted advertising, and potentially exploited for malicious purposes, such as phishing or targeted cyberattacks.

  4. Evasion of Privacy Tools: Font fingerprinting can bypass privacy tools like VPNs, cookie blockers, and incognito modes, as it relies on device-specific data that remains unaffected by these tools. Even if a user is taking steps to protect their privacy, font fingerprinting can still track them.

  5. Compliance Issues: In regions with strict privacy regulations (e.g., the European Union’s GDPR), font fingerprinting may violate user consent requirements. Users may not be aware that their devices are being fingerprinted, making it difficult for organizations to comply with data protection laws.

How to Prevent Font Fingerprint Leakage

Here are several ways to mitigate the risks of font fingerprinting:

1. Disable or Randomize Fonts

Some browsers allow users to disable certain font fingerprinting scripts or randomize the fonts that websites can access. This reduces the likelihood that a unique font fingerprint can be created.

2. Use Privacy-Focused Browsers

Browsers such as Tor and Brave provide privacy features that help block or randomize font fingerprinting attempts. These browsers typically block third-party tracking scripts, including font fingerprinting, ensuring that users remain anonymous.

3. Use Browser Extensions

Several extensions are available that help block or spoof font fingerprinting attempts. Extensions like Privacy Badger or CanvasBlocker can prevent scripts from detecting font details and help mitigate tracking.

4. Font Fingerprint Spoofing

Just as with other types of fingerprinting, spoofing or randomizing font fingerprints can be an effective way to protect privacy. Some browser extensions or privacy tools offer font spoofing features, making it harder for websites to detect which fonts are installed on your device.

5. Monitor and Manage Font Settings in Web Scraping

For web scrapers, managing font settings becomes critical to avoid detection. Many websites use font fingerprinting to detect bots, so scraping tools should configure browsers to either randomize or mimic real user settings. Tools like Scrapeless offer headless browser technology that can automatically adjust browser settings, including fonts, to ensure the scraping process remains undetected.

Conclusion

Font fingerprinting is a powerful technique for tracking users online by utilizing the unique fonts installed on their devices. Although it can be used for legitimate purposes, such as ad targeting and analytics, it raises significant privacy concerns. Users can mitigate the risks of font fingerprinting by using privacy-focused browsers, spoofing font fingerprints, and employing tools like Scrapeless to manage browser settings .

As privacy concerns continue to grow, it is essential for users and developers to be aware of the risks associated with font fingerprinting and take proactive measures to safeguard their online identities.

At Scrapeless, we only access publicly available data while strictly complying with applicable laws, regulations, and website privacy policies. The content in this blog is for demonstration purposes only and does not involve any illegal or infringing activities. We make no guarantees and disclaim all liability for the use of information from this blog or third-party links. Before engaging in any scraping activities, consult your legal advisor and review the target website's terms of service or obtain the necessary permissions.

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