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 …

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 …

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 …

Adversarial attacks on deep-learning models have been receiving increased attention in
recent years. Work in this area has mostly focused on gradient-based techniques, so-called …

Abstract Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial
examples, which are slightly perturbed input images which lead DNNs to make wrong …

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision,
their vulnerability to adversarial attacks remains a critical concern. Extensive research has …

Adversarial attacks in deep learning models, especially for safety-critical systems, are
gaining more and more attention in recent years, due to the lack of trust in the security and …

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 …

Many recent studies have shown that deep neural models are vulnerable to adversarial
samples: images with imperceptible perturbations, for example, can fool image classifiers. In …

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 …