The deep learning community has been trying to develop methods for training robust deep learning models that are not vulnerable to adversarial perturbations, ...

In this work, we survey some of the most important contributions in the field of adversarial robustness. We pay particular attention to the reasons why past ...

Deep neural networks have improved the performance of large-scale learning tasks such as image recognition and speech recognition. However, neural networks also ...

Nov 8, 2023 · The widespread success of deep learning in solving machine learning problems has fueled its adoption in many fields, from speech recognition ...

This tutorial seeks to provide a broad, hands-on introduction to this topic of adversarial robustness in deep learning.

Aug 24, 2021 · This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning, presenting a well-structured review of up-to-date techniques.

Nov 22, 2023 · In this work, we survey some of the most important contributions in the fi eld of adversarial robustness. We pay particular attention to the ...

Instead, it gives a short introduction to deep learning and its adversarial robustness, and provides a taste of what deep learning is, how to train a neural ...

Sep 7, 2023 · Abstract:We introduce the ARMOR_D methods as novel approaches to enhancing the adversarial robustness of deep learning models.

In this thesis, we discuss recent progress toward designing algorithms that exhibit desirable robustness properties.