Ad-versarial: Defeating Perceptual Ad-Blocking

Perceptual ad-blocking is a novel approach that uses visual cues to detect online advertisements. Compared to classical filter lists, perceptual ad-blocking is believed to be less prone to an arms race with web publishers and ad-networks. In this work we use techniques from adversarial machine learning to demonstrate that this may not be the case. We show that perceptual ad-blocking engenders 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 full visual-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. Our attacks exploit the unreasonably strong threat model that perceptual ad-blockers must survive. Finally, we evaluate a concrete set of attacks on an ad-blocker's internal ad-classifier by instantiating adversarial examples for visual systems in a real web-security context. For six ad-detection techniques, we create perturbed ads, ad-disclosures, and native web content that misleads perceptual ad-blocking with 100% success rates. For example, we demonstrate how a malicious user can upload adversarial content (e.g., a perturbed image in a Facebook post) that fools the ad-blocker into removing other users' non-ad content.