This tutorial seeks to provide a broad, hands-on introduction to this topic of adversarial robustness in deep learning. The goal is combine both a mathematical presentation and illustrative code examples …

Dec 15, 2021 · Adversarial robustness refers to a model’s ability to resist being fooled. Our recent work looks to improve the adversarial robustness of AI models, making them more impervious to …

Oct 30, 2024 · Adversarial robustness is dedicated to enhancing the security of machine learning models by making them resilient against malicious attacks.

This tutorial seeks to provide a broad, hands-on introduction to the topic concerning adversarial robustness: the widespread vulnerability of state-of-the-art deep learning models to adversarial …

How to make neural networks robust? Can we “fool” neural networks to misclassify? Can we design learning algorithms to get robustness guarantees? Can we verify that a given model is robust? What …

Feb 17, 2025 · Deep Neural Networks (DNNs) are vulnerable to adversarial attacks. Existing methods are devoted to developing various robust training strategies or regularizations to update the weights …

5 days ago · A comprehensive analysis of the trade-offs between robustness and model creativity, the computational overhead of adversarial training, and the broader implications of adversarial prompt …

The goal of RobustBench is to systematically track the real progress in adversarial robustness. There are already more than 3'000 papers on this topic, but it is still unclear which approaches really work …

Adversarial Robustness Toolbox (ART) provides tools that enable developers and researchers to evaluate, defend, and verify Machine Learning models and applications against adversarial threats. …

One of the most active areas of research for addressing these issues is adversarial robustness, a field that deals with the dependability of a neural network when coping with deliberately altered inputs.