arXiv:2410.07719v4 Announce Type: replace Abstract: Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing empirical and theoretical explorations fail to provide a satisfactory mechanistic interpretation of the phenomenon. By modeling adversarial training with momentum SGD as a discrete-time dynamical system, we propose a PAC-Bayesian analytical framework that proves time-resolved robust generalization bounds. Specifically, our framework tracks the closed-form evolution of the posterior mean and covarianc
The paper provides a theoretical framework for understanding robust generalization in adversarial training, addressing a persistent challenge in model reliability and security.
Understanding the learning dynamics of robust generalization is crucial for developing more secure and reliable AI systems, especially as AI deployment scales in critical applications.
This theoretical model improves the ability to predict and control robust overfitting, potentially leading to more efficient and effective adversarial training methods.
- · AI researchers
- · Cybersecurity sector
- · AI model developers
- · Adversarial attackers
- · Brute-force security approaches
Improved understanding and mitigation of robust overfitting in AI models.
Development of more fundamentally secure and reliable AI applications across various industries.
Increased trust and adoption of AI in sensitive domains due to enhanced security guarantees.
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Read at arXiv cs.LG