Taxonomy and Recent Advance of Game Theoretical Approaches in Adversarial Machine Learning: A Survey
Bibliographic record
Abstract
Carefully perturbing adversarial inputs degrades the performance of traditional machine learning (ML) models. Adversarial machine learning (AML) that takes adversaries into account during training and learning emerges as a valid technique to defend against attacks. Due to the complexity and uncertainty of adversaries’ attack strategies, researchers utilize game theory to study the interactions between an adversary and an ML system designer. By configuring different game rules and analyzing game outcomes in an adversarial game, it is possible to effectively predict attack strategies and to produce optimal defense strategies for the system designer. However, the literature still lacks a holistic review of adversarial games in AML. In this paper, we extend the scope of previous surveys and provide a thorough overview of existing game theoretical approaches in AML for adaptively defending against adversarial attacks. For evaluating these approaches, we propose a set of metrics to discuss their merits and drawbacks. Finally, based on our literature review and analysis, we raise several open problems and suggest interesting research directions worthy of special investigation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".