  Browser fingerprinting is an invasive and opaque stateless tracking
technique. Browser vendors, academics, and standards bodies have long struggled
to provide meaningful protections against browser fingerprinting that are both
accurate and do not degrade user experience. We propose FP-Inspector, a machine
learning based syntactic-semantic approach to accurately detect browser
fingerprinting. We show that FP-Inspector performs well, allowing us to detect
26% more fingerprinting scripts than the state-of-the-art. We show that an
API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce
website breakage by a factor of 2. We use FP-Inspector to perform a measurement
study of browser fingerprinting on top-100K websites. We find that browser
fingerprinting is now present on more than 10% of the top-100K websites and
over a quarter of the top-10K websites. We also discover previously unreported
uses of JavaScript APIs by fingerprinting scripts suggesting that they are
looking to exploit APIs in new and unexpected ways.
