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Over-parameterization and Adversarial Robustness in Neura...
[Submitted on 14 Jun 2024 (v1), last revised 25 Jun 2026 (this v · 2026-06-26 · via cs.LG updates on arXiv.org

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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 employed to evaluate the networks' robustness. Previous research has demonstrated that depending on the considered model, the algorithm employed to generate adversarial examples may not function properly, leading to overestimating the model's robustness. In this work, we empirically study the robustness of over-parameterized networks against adversarial examples. However, unlike the previous works, we also evaluate the considered attack's reliability to support the results' veracity. Our results show that over-parameterized networks are robust against adversarial attacks as opposed to their under-parameterized counterparts.

Submission history

From: Srishti Gupta [view email]
[v1] Fri, 14 Jun 2024 14:47:06 UTC (8,441 KB)
[v2] Wed, 17 Dec 2025 11:01:01 UTC (102 KB)
[v3] Thu, 25 Jun 2026 11:55:45 UTC (12,889 KB)