You Only Perturb Once: Bypassing (Robust) Ad-Blockers Using Universal Adversarial Perturbations
Bibliographic record
Abstract
Extensive academic effort has been put into the development of effective machine learning models that block advertising and tracking service (ATS) content. These ATS blockers leverage various features from websites, such as structural, content, flow, and JavaScript features, to develop accurate and robust models. However, establishing the robustness of these ATS blockers to evasion attacks is largely understudied, particularly in practical scenarios in which an adversary generates a single and cost-effective universal perturbation that renders ATS detection across websites ineffective at scale.In this paper, we show that recent ATS blockers using machine learning are not robust to a universal adversarial attack. Specifically, we propose an auditing framework (YOPO) that enables one to generate a single adversarial perturbation in a cost-effective manner. Our framework casts the generation of a universal perturbation into an optimization problem in a principled way; it enables an adversary to minimize the cost of manipulating various features in HTML content and to thwart ATS classification while constraining the perturbation size for each feature. We demonstrate that YOPO is capable of generating a universal perturbation that enables bypassing four seminal ATS blockers: AdGraph, WebGraph, AdFlush, and PageGraph, attaining success rates of up to 92.27%, 71.50%, 61.91%, and 85.81%, respectively. We also propose a practical and effective countermeasure against YOPO that only requires preprocessing training instances without large performance drops in ATS blocking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".