arXiv:2604.25965v2 Announce Type: replace-cross Abstract: Deep learning models are widely deployed in safety-critical domains, but remain vulnerable to adversarial attacks. In this paper, we study the adversarial robustness of NTK neural networks in the context of nonparametric regression. We establish minimax optimal rates for adversarial regression in Sobolev spaces and then show that NTK neural networks, trained via gradient flow with early stopping, can achieve this optimal rate. However, in the overfitting regime, we prove that the minimum norm interpolant is vulnerable to adversarial per
The paper details a significant step in understanding and mitigating adversarial vulnerabilities in deep learning, which is a pressing concern given the increasing deployment of AI in critical systems and ongoing research into AI safety.
A strategic reader should care because this research directly addresses a key limitation in AI robustness, impacting the trustworthiness and secure deployment of AI models across many sectors.
This research provides a theoretical foundation for understanding and potentially improving the adversarial robustness of neural networks, guiding future development towards more secure AI systems.
- · AI developers
- · Cybersecurity experts
- · Industries relying on AI for critical applications
- · Adversarial attackers
- · Organizations with vulnerable AI deployments
Increased research and development into robust AI architectures and training methods.
Improved security and reliability of AI systems integrated into infrastructure and decision-making processes.
Potential for new regulatory frameworks and industry standards for AI robustness, influencing market access and product development.
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Read at arXiv cs.LG