CISPA
Browse
cispa_all_2972.pdf (10.25 MB)

AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning

Download (10.25 MB)
conference contribution
posted on 2023-11-29, 18:11 authored by Florian Tramèr, Pascal Dupré, Rusak Gili, Giancarlo PellegrinoGiancarlo Pellegrino, Dan Boneh
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 the 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.

History

Preferred Citation

Florian Tramèr, Pascal Dupré, Rusak Gili, Giancarlo Pellegrino and Dan Boneh. AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning. In: ACM Conference on Computer and Communications Security (CCS). 2019.

Primary Research Area

  • Empirical and Behavioral Security

Secondary Research Area

  • Threat Detection and Defenses

Name of Conference

ACM Conference on Computer and Communications Security (CCS)

Legacy Posted Date

2019-10-07

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_2972, title = "AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning", author = "Tramèr, Florian and Dupré, Pascal and Gili, Rusak and Pellegrino, Giancarlo and Boneh, Dan", booktitle="{ACM Conference on Computer and Communications Security (CCS)}", year="2019", }

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC