Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
arXiv:2406.10090v3 Announce Type: replace Abstract: Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is considered one of the main suspects of the neural networks' vulnerability to adversarial example -- input samples crafted ad-hoc to induce a desired misclassification. Relevant literature has claimed contradictory remarks in support of and against the robustness of over-parameterized networks. These contradictory findings might be due to the failure of the attack employe
The paper is published amidst ongoing academic and industry efforts to understand and mitigate vulnerabilities in AI systems, especially as trust in AI for critical applications grows.
A strategic reader should care because understanding adversarial robustness is crucial for deploying reliable AI, particularly in sensitive areas where security and predictability are paramount.
This paper re-evaluates conflicting findings on over-parameterization and adversarial robustness, potentially clarifying design principles for more secure neural networks and influencing future AI development strategies.
- · AI security researchers
- · Developers of robust AI systems
- · Industries reliant on secure AI
- · Malicious actors targeting AI
- · Developers of vulnerable AI systems
Increased focus on designing neural networks that are inherently more resilient to adversarial attacks.
Improved trustworthiness of AI models could accelerate adoption in high-stakes applications like autonomous vehicles or critical infrastructure.
The development of robust AI could reduce the incentive for adversarial attacks, shifting cybersecurity concerns to other vectors.
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Read at arXiv cs.LG