  The rise of ad-blockers is viewed as an economic threat by online publishers,
especially those who primarily rely on ad- vertising to support their services.
To address this threat, publishers have started retaliating by employing
ad-block detectors, which scout for ad-blocker users and react to them by
restricting their content access and pushing them to whitelist the website or
disabling ad-blockers altogether. The clash between ad-blockers and ad-block
detectors has resulted in a new arms race on the web. In this paper, we present
the first systematic measurement and analysis of ad-block detection on the web.
We have designed and implemented a machine learning based tech- nique to
automatically detect ad-block detection, and use it to study the deployment of
ad-block detectors on Alexa top- 100K websites. The approach is promising with
precision of 94.8% and recall of 93.1%. We characterize the spectrum of
different strategies used by websites for ad-block detection. We find that most
of publishers use fairly simple passive ap- proaches for ad-block detection.
However, we also note that a few websites use third-party services, e.g.
PageFair, for ad-block detection and response. The third-party services use
active deception and other sophisticated tactics to de- tect ad-blockers. We
also find that the third-party services can successfully circumvent ad-blockers
and display ads on publisher websites.
