Research on adversarial attacks analyses how to slightly manipulate patterns like images to
make a classifier believe it recogises a pattern with a wrong label, although the correct label …

Adversarial examples are intentionally perturbed images that mislead classifiers. These
images can, however, be easily detected using denoising algorithms, when high-frequency …

Nowadays, autonomous driving has attracted much attention from both industry and
academia. Convolutional neural network (CNN) is a key component in autonomous driving …

Deep neural network has been widely used in pattern recognition and speech processing,
but its vulnerability to adversarial attacks also proverbially demonstrated. These attacks …

Deep neural network is the main research branch in artificial intelligence and suitable for
many decision-making fields. Autonomous driving and unmanned vehicle often depend on …

The rapid development of deep neural networks (DNN) has promoted the widespread
application of image recognition, natural language processing, and autonomous driving …

Adversarial attacks in the physical world can harm the robustness of detection models.
Evaluating the robustness of detection models in the physical world can be challenging due …

Despite having achieved excellent performance on various tasks, deep neural networks
have been shown to be susceptible to adversarial examples, ie, visual inputs crafted with …

Physical adversarial attacks threaten to fool object detection systems, but reproducible
research on the real-world effectiveness of physical patches and how to defend against …

In this paper, we focus on studying robust deep stereo vision of autonomous driving systems
and counteracting adversarial attacks against it. Autonomous system operation requires real …