# A First Look at Ad-block Detection: A New Arms Race on the Web

arXiv: https://arxiv.org/abs/1605.05841 · HTML: https://mlsanity.com/paper/1605.05841/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2016.

## Built on by (most cited first, 16 of 16)

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- [Mathematical Models of Human Drivers Using Artificial Risk Fields](https://mlsanity.com/paper/2205.12722/) (2022)
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## Abstract (arXiv)

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.