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 …

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

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 …

Gradient-based methods are becoming increasingly important for computer graphics,
machine learning, and computer vision. The ability to compute gradients is crucial to …

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 …

We present a systematic study of the transferability of adversarial attacks on state-of-the-art
object detection frameworks. Using standard detection datasets, we train patterns that …

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 …

The problem of adversarial robustness has been studied extensively for neural networks.
However, for boosted decision trees and decision stumps there are almost no results, even …

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 …