  Efforts by online ad publishers to circumvent traditional ad blockers towards
regaining fiduciary benefits, have been demonstrably successful. As a result,
there have recently emerged a set of adblockers that apply machine learning
instead of manually curated rules and have been shown to be more robust in
blocking ads on websites including social media sites such as Facebook. Among
these, AdGraph is arguably the state-of-the-art learning-based adblocker. In
this paper, we develop A4, a tool that intelligently crafts adversarial samples
of ads to evade AdGraph. Unlike the popular research on adversarial samples
against images or videos that are considered less- to un-restricted, the
samples that A4 generates preserve application semantics of the web page, or
are actionable. Through several experiments we show that A4 can bypass AdGraph
about 60% of the time, which surpasses the state-of-the-art attack by a
significant margin of 84.3%; in addition, changes to the visual layout of the
web page due to these perturbations are imperceptible. We envision the
algorithmic framework proposed in A4 is also promising in improving adversarial
attacks against other learning-based web applications with similar
requirements.
