Deep neural networks (DNN) have achieved unprecedented success in numerous machine
learning tasks in various domains. However, the existence of adversarial examples raises …

Deep neural networks (DNNs) have shown huge superiority over humans in image
recognition, speech processing, autonomous vehicles, and medical diagnosis. However …

We demonstrate, theoretically and empirically, that adversarial robustness can significantly
benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of …

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …

We show that there may exist an inherent tension between the goal of adversarial
robustness and that of standard generalization. Specifically, training robust models may not …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …

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

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 …

Classification models are often used to make decisions that affect humans: whether to
approve a loan application, extend a job offer, or provide insurance. In such applications …

Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …