  Browser fingerprinting is a stateless identification technique based on
browser properties. Together, they form an identifier that can be collected
without users' notice and has been studied to be unique and stable. As this
technique relies on browser properties that serve legitimate purposes, the
detection of this technique is challenging. While several studies propose
classification techniques, none of these are publicly available, making them
difficult to reproduce. This paper proposes a new browser fingerprinting
detection technique. Based on an incremental process, it relies on both
automatic and manual decisions to be both reliable and fast. The automatic step
matches API calls similarities between scripts while the manual step is
required to classify a script with different calls. We publicly share our
algorithm and implementation to improve the general knowledge on the subject.
