content: This is not limited to WebGL. I suggest people read the paper. Here's the PDF Snippets:  Now let us talk about browser fingerprinting works. Mow- ery et al. [32] are probably one of the very early works in proposing canvas-based fingerprinting. Some other works [31, 33] focus on fingerprinting browser JavaScript engine. Nakibly et al. [34], a position paper, propose several hardware-based tracking including microphone, motion sensor and GPU. Their GPU tracking only includes timing-based features, less reli- able than the technique in the paper. Laperdrix et al. [26], i.e., AmIUnique, perform a most extensive study on browser fingerprinting with 17 attributes and we have compared with them throughout our paper. Boda et al. [14] attempts to achieve cross-browser tracking, but their features are old ones from single-browser tracking including IP address. As discussed, IP addresses are unreliable when a machine is using a DHCP, behind a NAT, or moved to a new location like a laptop. ... .. In conclusion, we have proposed a novel browser finger- printing that can identify not only users behind one browser but also these that use different browsers on the same machine. Our approach adopts OS and hardware levels features including graphic cards exposed by WebGL, audio stack by Audio- Context, and CPU by hardwareConcurrency.