A First Look at Ad-block Detection: A New Arms Race on the Web
Muhammad Haris Mughees, Zhiyun Qian, Zubair Shafiq, Karishma Dash, Pan, Hui

TL;DR
This paper systematically measures and analyzes the use of ad-block detection on top websites, revealing a spectrum of strategies from simple passive methods to sophisticated third-party services that can circumvent ad-blockers.
Contribution
It introduces a machine learning technique for automatic detection of ad-block detection and provides the first comprehensive analysis of its deployment on popular websites.
Findings
Most publishers use simple passive ad-block detection methods.
Some websites employ third-party services with active deception tactics.
Third-party services can successfully circumvent ad-blockers.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
