Nowadays, a vast number of websites, platforms, and online services collect user device information, browser data, and network connection details to achieve user identification. This collection of identifying information is referred to as a "digital fingerprint." Websites rely on these fingerprint data as part of their security mechanisms to determine whether a visiting user exhibits suspicious behavior.
The specific parameters analyzed by security systems vary across different websites. To complete a network connection and load page content properly, a browser automatically transmits over 50 device-related parameters to a website, each of which can potentially form part of a digital fingerprint.
Additionally, a browser may be instructed to generate simple 2D or 3D images. Due to differences in how different devices execute image generation tasks, a unique hash value is ultimately produced. This hash value can clearly distinguish the device from other website visitors. Through technologies like Canvas and WebGL, hardware fingerprinting operates precisely on this principle.
Even making minor adjustments to some of the characteristic information that the browser transmits to a website’s security system cannot prevent a familiar user from being recognized. You might change browsers, modify the time zone, or adjust the screen resolution, but even if all these changes are made simultaneously, the probability of being accurately identified by the website remains high.
Fingerprinting technology, along with other anti-scraping measures such as rate limiting, geolocation verification, WAF (Web Application Firewall), verification challenges, and CAPTCHA, is primarily designed to protect websites from malicious interactions by automated programs. Multi-account browsers equipped with high-quality fingerprint spoofing capabilities can effectively bypass a website’s security detection, thereby improving the efficiency of web scraping—making the data collection process faster and the results more reliable.