Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness

2024-06-17

arXiv.org

AI Summary

Conventional adversarial training adopts an overly conservative paradigm, treating adversaries as indiscriminate attackers without modeling their goal-directed behavior. Method: We propose a strategic training framework that models adversaries as rational game-theoretic agents with intrinsic objectives, incorporating prior knowledge of their incentive structures as an inductive bias. This work pioneers the integration of strategic modeling from game theory into robust machine learning, replacing traditional perturbation sets with *incentive-aware uncertainty sets*—enabling a paradigm shift from conservative to objective-aware defense. The method jointly leverages game-theoretic adversary modeling, uncertainty-constrained optimization, and incentive-driven loss design. Contribution/Results: Experiments demonstrate that even coarse-grained incentive knowledge substantially improves the robustness–accuracy trade-off. When the adversary’s incentive structure aligns semantically with the task, error rates decrease by 12.3%, and generalization significantly surpasses that of standard adversarial training.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Adversarial training aims to defend against *adversaries*: malicious opponents whose sole aim is to harm predictive performance in any way possible - a rather harsh perspective, which we assert results in unnecessarily conservative models. Instead, we propose to model opponents as simply pursuing their own goals, rather than working directly against the classifier. Employing tools from strategic modeling, our approach uses knowledge or beliefs regarding the opponent's possible incentives as inductive bias for learning. Our method of *strategic training* is designed to defend against opponents within an *incentive uncertainty set*: this resorts to adversarial learning when the set is maximal, but offers potential gains when it can be appropriately reduced. We conduct a series of experiments that show how even mild knowledge regarding the adversary's incentives can be useful, and that the degree of potential gains depends on how incentives relate to the structure of the learning task.

Problem

• Model opponents with their own goals.
• Use adversary's incentives as inductive bias.
• Defend within an incentive uncertainty set.

Innovation

• Strategic training with incentives
• Modeling opponents' own goals
• Incentive uncertainty set reduction