Non-Parametric Probabilistic Robustness: A Conservative Risk Estimator under Unknown Perturbation Distributions
arXiv:2511.17380v2 Announce Type: replace-cross Abstract: Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motivating the recent proposal of probabilistic robustness (PR) as a complementary alternative to adversarial robustness (AR). However, existing PR formulations assume a fixed and known perturbation distribution, an unrealistic expectation in practice. To address this limitation, we propose non-parametric probabilistic robustness (NPPR), a more practical PR metric that does not rely on any predefi
This research addresses a critical limitation in existing deep learning robustness methods, building on current trends to make AI more reliable in real-world, unpredictable environments.
Improving the robustness of AI models against unknown perturbations is crucial for their deployment in sensitive applications, enhancing trust and accelerating adoption across industries.
The proposed non-parametric probabilistic robustness (NPPR) offers a more practical and reliable metric for assessing AI model resilience without requiring predefined perturbation distributions.
- · AI developers
- · Industries deploying AI in critical systems
- · Cybersecurity researchers
- · Adversarial attackers relying on known perturbation patterns
- · AI models with poor generalization to unknown conditions
AI systems will become more resilient to unforeseen data variations and malicious attacks.
Increased reliability will expand AI's application into highly sensitive domains like autonomous vehicles and medical diagnostics.
Greater confidence in AI robustness could shift regulatory focus from adversarial specifics to broader, unknown perturbation handling.
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Read at arXiv cs.LG