arXiv:2211.14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training data and unseen perturbed test data. While DNNs can fit perturbed training data, generalizing to perturbed test data remains a significant challenge. This motivates the study of generalization guarantees from a learning theory perspective. This paper focuses on adversarial Rademacher complexity (ARC), first introduced by Khim and Loh (2018) and Yin et al. (2019). Their work primarily addressed linear
The continuous evolution of AI models and their increasing deployment in critical applications necessitate deeper understanding and mitigation of vulnerabilities, making research into adversarial robustness timely.
This research provides theoretical foundations for understanding and improving the robustness of deep neural networks against adversarial attacks, which is crucial for AI security and reliability across all applications.
The focus on adversarial Rademacher complexity offers a more robust theoretical framework for measuring and enhancing AI model generalization under attack, moving beyond empirical fixes.
- · AI security researchers
- · AI infrastructure providers
- · High-stakes AI application developers
- · Adversarial attackers
- · AI models lacking robustness
- · AI systems with poor generalization
Improved theoretical understanding of AI robustness will lead to more secure and deployable AI systems.
Enhanced adversarial resilience could accelerate AI adoption in sensitive sectors like defense and finance.
A more secure AI ecosystem might shift resources towards exploring new attack vectors and defense mechanisms, creating a continuous arms race.
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Read at arXiv cs.LG