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 …

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 …

An adversary can fool deep neural network object detectors by generating adversarial
noises. Most of the existing works focus on learning local visible noises in an adversarial" …

Physical adversarial attacks against object detectors have seen increasing success in recent
years. However, these attacks require direct access to the object of interest in order to apply …

In this paper, we tackle the issue of physical adversarial examples for object detectors in the
wild. Specifically, we proposed to generate adversarial patterns to be applied on vehicle …

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 …

Deep neural networks (DNNs) are vulnerable to adversarial examples—maliciously crafted
inputs that cause DNNs to make incorrect predictions. Recent work has shown that these …

Recent research shows that neural networks models used for computer vision (eg, YOLO
and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real …

An adversarial example is an example that has been adjusted to produce the wrong label
when presented to a system at test time. If adversarial examples existed that could fool a …