In this paper, we study physical adversarial attacks on object detectors in the wild. Previous
works mostly craft instance-dependent perturbations only for rigid or planar objects. To this …

Physical adversarial attacks in object detection have attracted increasing attention.
However, most previous works focus on hiding the objects from the detector by generating …

In this paper, we conduct an intriguing experimental study about the physical adversarial
attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide …

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 …

Most prior works on physical adversarial attacks mainly focus on the attack performance but
seldom enforce any restrictions over the appearance of the generated adversarial patches …

To perform adversarial attacks in the physical world, many studies have proposed
adversarial camouflage, a method to hide a target object by applying camouflage patterns …

Recently, physical adversarial attacks have been presented to evade DNNs-based object
detectors. To ensure the security, many scenarios are simultaneously deployed with visible …

Recently Adversarial Examples (AEs) that deceive deep learning models have been a topic
of intense research interest. Compared with the AEs in the digital space, the physical …

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 …

Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is
necessary to evaluate their robustness against adversarial examples in the real world …