> For the complete documentation index, see [llms.txt](https://help.gpmloginapp.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.gpmloginapp.com/gpm-login-global-en/getting-started/understanding-fingerprints/hardware-canvas.md). # Hardware: Canvas Canvas is an HTML5 API used to render 2D graphics within the browser, such as drawing shapes (circles, triangles), displaying text as images, and rendering other visual elements. These 2D images can exhibit minute, subtle differences when rendered by different graphics cards (these variations are often imperceptible to the naked eye). Therefore, Canvas can be utilized as a parameter to distinguish between different hardware configurations. Anti-detect browsers employ a technique called "noise injection" (or simply "noise") to slightly alter the rendered image compared to the original native rendering. #### Characteristics of the Noise Technique: * Uniqueness vs. Replication: The noise technique cannot make an image rendered by Graphics Card A look exactly like an image rendered by Graphics Card B. It is similar to plastic surgery: you cannot make Person A look completely identical to Person B in every detail; you can only make Person A look different from their original self. * High Entropy: This technique helps generate a high level of diversity and uniqueness for the browser fingerprint. * Detectability: However, noise injection can sometimes be detected by large-scale systems that collect and analyze big data for verification purposes. If we consider only the Canvas parameter, there are tens of millions of identical computers worldwide (using the exact same graphics card, operating system, and browser version). But when noise is applied, the uniqueness score becomes abnormally high. A Simple Example: Facebook has billions of daily visitors, and they collect and store Canvas hash data. When you visit Facebook, if your Canvas fingerprint matches that of many other users, it is considered normal. However, if your fingerprint is entirely unique, it becomes highly suspicious and practically impossible (as it is highly unlikely that you are using a combination of a graphics card, OS, and browser that absolutely no one else among billions of users possesses). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://help.gpmloginapp.com/gpm-login-global-en/getting-started/understanding-fingerprints/hardware-canvas.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.