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

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 …

Object detection is considered as one of the most important applications of deep learning.
However, the object detection techniques lose their effectiveness and reliability when they …

Compared to query-based black-box attacks, transfer-based black-box attacks do not
require any information of the attacked models, which ensures their secrecy. However, most …

Adversarial examples have been demonstrated to threaten many computer vision tasks
including object detection. However, the existing attacking methods for object detection have …

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 …

Object detection is an important vision task and has emerged as an indispensable
component in many vision system, rendering its robustness as an increasingly important …

Advanced patch attacks (PAs) on object detection in natural images have pointed out the
great safety vulnerability in methods based on deep neural networks (DNNs). However, little …

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 …

Adversarial attacks of deep neural networks have been intensively studied on image, audio,
and natural language classification tasks. Nevertheless, as a typical while important real …