Adversarial Robustness Toolbox (ART) is a Python library supporting developers and
researchers in defending Machine Learning models (Deep Neural Networks, Gradient …

DeepRobust is a PyTorch platform for generating adversarial examples and building robust
machine learning models for different data domains. Users can easily evaluate the attack …

Evaluating robustness of machine-learning models to adversarial examples is a challenging
problem. Many defenses have been shown to provide a false sense of robustness by …

As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Even todays most advanced machine learning models are easily fooled by almost
imperceptible perturbations of their inputs. Foolbox is a new Python package to generate …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

The field of defense strategies against adversarial attacks has significantly grown over the
last years, but progress is hampered as the evaluation of adversarial defenses is often …

Deep learning has shown impressive performance on hard perceptual problems. However,
researchers found deep learning systems to be vulnerable to small, specially crafted …

Abstract The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled
research towards building robust models. While most Adversarial Training algorithms aim at …