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 …

Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier
days of conceptual theories, to being an integral part of today's technological society. Rapid …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

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 …

Contrastive learning (CL) can learn generalizable feature representations and achieve state-
of-the-art performance of downstream tasks by finetuning a linear classifier on top of it …

Graph neural networks (GNNs) which apply the deep neural networks to graph data have
achieved significant performance for the task of semi-supervised node classification …

Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable
Diffusion, less research attention is paid to its adversarial robustness. In this work, we study …

It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-
called physical adversarial examples deceive DNN-based decision makers by attaching …

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 …

Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness
of deep neural networks against adversarial attacks. It is built on min-max optimization …