In recent years, researches on adversarial attacks and defense mechanisms have obtained
much attention. It's observed that adversarial examples crafted with small malicious …

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 …

Adversarial patch is an important form of real-world adversarial attack that brings serious
risks to the robustness of deep neural networks. Previous methods generate adversarial …

Object detection plays a key role in many security-critical systems. Adversarial patch attacks,
which are easy to implement in the physical world, pose a serious threat to state-of-the-art …

Localized adversarial patches aim to induce misclassification in machine learning models
by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be …

Patch attacks, one of the most threatening forms of physical attack in adversarial examples,
can lead networks to induce misclassification by modifying pixels arbitrarily in a continuous …

Though deep neural networks (DNNs) have demonstrated excellent performance in
computer vision, they are susceptible and vulnerable to carefully crafted adversarial …

Adversarial patch attacks are among one of the most practical threat models against real-
world computer vision systems. This paper studies certified and empirical defenses against …

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible
adversarial perturbations. As a complementary type of adversary, patch attacks that …

The adversarial patch attack against image classification models aims to inject adversarially
crafted pixels within a restricted image region (ie, a patch) for inducing model …