Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
arXiv:2606.01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations. In this work, we identify that part of this vulnerability stems from the sensitivity of the widely used fully connected (FC) classifiers to such perturbations. In contrast, simple $\ell_2$ distance-based classifiers exhibit significantly greater robustness. We provide thorough theoretical and empirical analysis showing that while FC classifiers' high sensitivity makes them discriminative, it also makes them vulnerable. Conversely, $\ell_2$-classifiers' insensitivity gra
This research highlights a fundamental trade-off in neural network design, occurring as AI systems become more ubiquitous and their security implications more pressing.
Understanding the inherent vulnerabilities of neural networks and exploring alternative designs is crucial for developing robust and trustworthy AI systems, especially in sensitive applications.
This research suggests a potential re-evaluation of commonly used classifier designs in neural networks, favoring robustness over pure discriminability in certain contexts.
- · AI security researchers
- · Developers of robust AI systems
- · Industries requiring high AI trustworthiness
- · Developers prioritizing raw discriminability
- · Systems highly reliant on standard FC classifiers
Increased focus on adversarial robustness in AI research and development.
New architectural paradigms for neural networks that balance performance and security.
More secure and deployable AI in critical infrastructure and defense applications.
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