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Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Srishti Gupta, Zhang Chen, Luca Demetrio +9
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is consi…
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
João Borges S. Carvalho, Victor Jimenez Rodriguez, Alessandro Torcinovich +4
The robustness of algorithms against covariate shifts is a fundamental problem with critical implications for the deployment of machine learning algorithms in the real world. Curre…
On the Robustness of Adversarial Training Against Uncertainty Attacks
Emanuele Ledda, Giovanni Scodeller, Daniele Angioni +5
In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of i…
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
Antonio Emanuele CinÃ, Jérôme Rony, Maura Pintor +5
Adversarial examples are typically optimized with gradient-based attacks. While novel attacks are continuously proposed, each is shown to outperform its predecessors using differen…
-zero: Gradient-based Optimization of -norm Adversarial Examples
Antonio Emanuele CinÃ, Francesco Villani, Maura Pintor +3
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider - and -norm constraints to craft i…
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
Antonio Emanuele CinÃ, Kathrin Grosse, Sebastiano Vascon +4
Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at…