# AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning

arXiv: https://arxiv.org/abs/1811.03194 · HTML: https://mlsanity.com/paper/1811.03194/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 14 of 14)

- [Are aligned neural networks adversarially aligned?](https://mlsanity.com/paper/2306.15447/) (2023)
- ["Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice](https://mlsanity.com/paper/2212.14315/) (2022)
- [Evading Black-box Classifiers Without Breaking Eggs](https://mlsanity.com/paper/2306.02895/) (2023)
- [Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning](https://mlsanity.com/paper/2210.13660/) (2022)
- [MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack](https://mlsanity.com/paper/2211.08008/) (2022)
- [Real-time Detection of Practical Universal Adversarial Perturbations](https://mlsanity.com/paper/2105.07334/) (2021)
- [Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally](https://mlsanity.com/paper/2310.03614/) (2023)
- [Realizable Universal Adversarial Perturbations for Malware](https://mlsanity.com/paper/2102.06747/) (2021)
- [Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems](https://mlsanity.com/paper/2210.03297/) (2022)
- [Jacobian Regularization for Mitigating Universal Adversarial Perturbations](https://mlsanity.com/paper/2104.10459/) (2021)
- [Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks](https://mlsanity.com/paper/2204.08726/) (2022)
- [DeepMoM: Robust Deep Learning With Median-of-Means](https://mlsanity.com/paper/2105.14035/) (2021)
- [AutoFR: Automated Filter Rule Generation for Adblocking](https://mlsanity.com/paper/2202.12872/) (2022)
- [Is Certifying ℓp Robustness Still Worthwhile?](https://mlsanity.com/paper/2310.09361/) (2023)

## Abstract (arXiv)

Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. On one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For seven ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users' non-ad content. Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams.