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

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Large language models are now tuned to align with the goals of their creators, namely to be"
helpful and harmless." These models should respond helpfully to user questions, but refuse …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

We identify a trade-off between robustness and accuracy that serves as a guiding principle
in the design of defenses against adversarial examples. Although this problem has been …

Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …

We show how to turn any classifier that classifies well under Gaussian noise into a new
classifier that is certifiably robust to adversarial perturbations under the L2 norm. While this" …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

Adversarial examples are perturbed inputs designed to fool machine learning models.
Adversarial training injects such examples into training data to increase robustness. To …

Adversarial training, in which a network is trained on adversarial examples, is one of the few
defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high …