Deep Neural Networks (DNNs) are demonstrated to be vulnerable to adversarial examples,
which are elaborately crafted to fool learning models. Since the accuracy and robustness of …

Making learners robust to adversarial perturbation at test time (ie, evasion attacks finding
adversarial examples) or training time (ie, data poisoning attacks) has emerged as a …

Modern image classification systems are often built on deep neural networks, which suffer
from adversarial examples--images with deliberately crafted, imperceptible noise to mislead …

Deep neural networks are shown to be vulnerable to adversarial attacks. This motivates
robust learning techniques, such as the adversarial training, whose goal is to learn a …

This work studies the adversarial robustness of parametric functions composed of a linear
predictor and a nonlinear representation map. Our analysis relies on sparse local …

Abstract Polynomial Networks (PNs) have demonstrated promising performance on face and
image recognition recently. However, robustness of PNs is unclear and thus obtaining …

We develop and study new adversarial perturbations that enable an attacker to gain control
over decisions in generic Artificial Intelligence (AI) systems including deep learning neural …

Training machine learning models that are robust against adversarial inputs poses
seemingly insurmountable challenges. To better understand adversarial robustness, we …

A substantial body of empirical work documents the lack of robustness in deep learning
models to adversarial examples. Recent theoretical work proved that adversarial examples …

Deep learning models have been shown to be successful in accelerating MRI
reconstruction, over traditional methods. However, it has been observed that these methods …