Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
This addresses the challenge of practical, efficient ad blocking for web users by offering a perceptual alternative to block lists, with incremental improvements in handling non-English and first-party ads.
The paper tackles the problem of in-browser ad blocking by introducing Percival, a lightweight deep learning-based system embedded in the browser's rendering pipeline, which achieves 96.76% accuracy in replicating EasyList rules with only a 4.55% performance overhead.
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 blocking based on applying machine learning for image classification to flag potential ads. Our implementation inside both Chromium and Brave browsers shows only a minor rendering performance overhead of 4.55%, 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%. To show the versatility of the Percival's approach we present case studies that demonstrate that Percival 1) does surprisingly well on ads in languages other than English; 2) Percival also performs well on blocking first-party Facebook ads, which have presented issues for other ad blockers. Percival proves that image-based perceptual ad blocking is an attractive complement to today's dominant approach of block lists