In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

It is common practice in deep learning to use overparameterized networks and train for as
long as possible; there are numerous studies that show, both theoretically and empirically …

Adversarial training and its variants have become de facto standards for learning robust
deep neural networks. In this paper, we explore the landscape around adversarial training in …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

We propose a method to learn deep ReLU-based classifiers that are provably robust against
norm-bounded adversarial perturbations on the training data. For previously unseen …

This paper investigates strategies that defend against adversarial-example attacks on image-
classification systems by transforming the inputs before feeding them to the system …

Adversarial examples that fool machine learning models, particularly deep neural networks,
have been a topic of intense research interest, with attacks and defenses being developed …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Standard methods for generating adversarial examples for neural networks do not
consistently fool neural network classifiers in the physical world due to a combination of …