Modern self-driving perception systems have been shown to improve upon processing
complementary inputs such as LiDAR with images. In isolation, 2D images have been found …

Object detection is an important computer vision task with plenty of real-world applications;
therefore, how to enhance its robustness against adversarial attacks has emerged as a …

With the adversarial attack of convolutional neural networks (CNNs), we are able to
generate adversarial patches to make an aircraft undetectable by object detectors instead of …

In the fields of deep learning and computer vision, the security of object detection models
has received extensive attention. Revealing the security vulnerabilities resulting from …

Research shows that deep neural networks are vulnerable to adversarial examples due to
the highly linear nature of deep neural networks (DNNs). Therefore, adversarial examples …

Adversarial data examples have drawn significant attention from the machine learning and
security communities. A line of work on tackling adversarial examples is certified robustness …

Recent advances in computer vision take advantage of adversarial data augmentation to
ameliorate the generalization ability of classification models. Here, we present an effective …

The adversarial perturbation for object detectors has drawn increasing attention due to the
application in video surveillance and autonomous driving. However, few works have …

Deep neural network (DNN) based object detection has become an integral part of
numerous cyber-physical systems, perceiving physical environments and responding …

The 2D object detection in clean images has been a well studied topic, but its vulnerability
against adversarial attack is still worrying. Existing work [1] has improved robustness of …