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Conference Paper
AdVersa: Adversarially-Robust and Practical Ad and Tracker Blocking in the Wild
- Authors
- Chaejin Lim, Kiho Lee, Beomjin Jin, Heewon Baek, Hyoungshick Kim
- Issue Date
- 2026-04
- Citation
- The Web Conference (WWW) 2026, pp.3519-3530
- Publisher
- ACM
- Language
- English
- Type
- Conference Paper
- Abstract
- While machine learning has significantly advanced ad and tracker detection, existing systems face critical challenges in practice. They are vulnerable to adversarial attacks (57-92% evasion rates), fail to generalize to unseen domains due to data contamination, and suffer performance degradation over time, requiring costly retraining. To address these challenges, we present AdVersa, a client-side framework for robust and practical ad and tracker blocking. AdVersa leverages novel, hard-to-perturb latent features from code and URL embeddings to deliver state-of-the-art performance. On a 2.74M-request dataset, our results show that AdVersa achieves a 98.23% F1 score, twice the robustness against adversarial attacks, and strong generalization to unseen domains (91.47% F1 score). For sustainable protection, we demonstrate that a low-cost pseudo-labeling strategy can maintain near-optimal accuracy, reducing maintenance overhead by over 99.8% compared to filter-list curation. Finally, we implement AdVersa as a lightweight, standalone client-side application that ensures user privacy by operating without external dependencies.
- Keyword
- Ad blocking, Web tracking, Machine learning, Web security and privacy
- KSP Keywords
- Ad blocking, Adversarial Attacks, Art performance, Low-cost, Over time, User privacy, Web security and privacy, Web tracking, data contamination, latent features, machine Learning
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
