Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
CoRR(2024)
摘要
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.
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