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 …

Deep neural networks (DNNs) have shown remarkable performance improvements on
vision-related tasks such as object detection or image segmentation. Despite their success …

Neural networks are known to be susceptible to adversarial samples: small variations of
natural examples crafted to deliberatelymislead the models. While they can be easily …

Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-
armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson …

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 robustness of neural networks to adversarial examples has received great attention due
to security implications. Despite various attack approaches to crafting visually imperceptible …

Modern autonomous driving systems rely heavily on deep learning models to process point
cloud sensory data; meanwhile, deep models have been shown to be susceptible to …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing
works have mostly focused on either digital adversarial examples created via small and …

As self-driving systems become better, simulating scenarios where the autonomy stack may
fail becomes more important. Traditionally, those scenarios are generated for a few scenes …

Though it is well known that the performance of deep neural networks (DNNs) degrades
under certain light conditions, there exists no study on the threats of light beams emitted from …