PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning

Website Maintenance Alert

Due to scheduled maintenance, the USENIX website may not be available on Monday, March 17, from 10:00 am–6:00 pm Pacific Daylight Time (UTC -7). We apologize for the inconvenience and thank you for your patience.

If you would like to register for NSDI '25, SREcon25 Americas, or PEPR '25, please complete your registration before or after this time period.

Authors: 

Zainul Abi Din, UC Davis; Panagiotis Tigas, University of Oxford; Samuel T. King, UC Davis, Bouncer Technologies; Benjamin Livshits, Brave Software, Imperial College London

Abstract: 

In this paper we present PERCIVAL, a browser-embedded, lightweight, deep learning-powered ad blocker. PERCIVAL embeds itself within the browser’s image rendering pipeline, which makes it possible to intercept every image obtained during page execution and to perform image classification based blocking to flag potential ads. Our implementation inside both Chromium and Brave browsers shows only a minor rendering performance overhead of 4.55%, for Chromium, and 19.07%, for Brave browser, demonstrating the feasibility of deploying traditionally heavy models (i.e. deep neural networks) inside the critical path of the rendering engine of a browser. We show that our image-based ad blocker can replicate EasyList rules with an accuracy of 96.76%. Additionally, PERCIVAL does surprisingly well on ads in languages other than English and also performs well on blocking first-party Facebook ads, which have presented issues for rule-based ad blockers. PERCIVAL proves that image-based perceptual ad blocking is an attractive complement to today’s dominant approach of block lists.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX

Presentation Video