Results 41 to 50 of about 1,209,773 (317)

Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

IEEE Open Journal of Vehicular Technology, 2023
Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious ...
Mansi Girdhar, Junho Hong, John Moore
semanticscholar   +1 more source

Online Adversarial Attacks

, 2021
Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream. In this paper, we formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases: attackers must operate under partial knowledge of the target model ...
Mladenovic, Andjela, Bose, Avishek Joey, Berard, Hugo, Hamilton, William L., Lacoste-Julien, Simon, Vincent, Pascal, Gidel, Gauthier   +6 more
openaire   +2 more sources

Robustness of Deep Learning Models for Vision Tasks

Applied Sciences, 2023
In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks.
Youngseok Lee, Jongweon Kim
doaj   +1 more source

Superclass Adversarial Attack

, 2022
ICML Workshop 2022 on Adversarial Machine Learning ...
Kumano, Soichiro, Kera, Hiroshi, Yamasaki, Toshihiko   +2 more
openaire   +2 more sources

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Sensors, 2023
Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks.
K. T. Yasas Mahima, Asanka Perera, Sreenatha Anavatti, Matt Garratt   +3 more
doaj   +1 more source

Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks

, 2020
Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them.
Li, Bo, Shan, Shawn, Wang, Bolun, Wenger, Emily, Zhao, Ben Y., Zheng, Haitao   +5 more
core   +1 more source

Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems [PDF]

IEEE Internet of Things Journal
The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultrareliability, low latency, and high throughput.
Bui Duc Son, Nguyen Tien Hoa, Trinh Van Chien, Waqas Khalid, M. Ferrag, Wan Choi, M. Debbah   +6 more
semanticscholar   +1 more source

Robust Tracking Against Adversarial Attacks [PDF]

, 2020
While deep convolutional neural networks (CNNs) are vulnerable to adversarial attacks, considerably few efforts have been paid to construct robust deep tracking algorithms against adversarial attacks. Current studies on adversarial attack and defense mainly reside in a single image. In this work, we first attempt to generate adversarial examples on top
Jia, Shuai, Ma, Chao, Song, Yibing, Yang, Xiaokang   +3 more
openaire   +2 more sources

Boosting 3D Adversarial Attacks With Attacking on Frequency

IEEE Access, 2022
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks in the image domain. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest.
Binbin Liu, Jinlai Zhang, Jihong Zhu
doaj   +1 more source

Exploring Diverse Feature Extractions for Adversarial Audio Detection

IEEE Access, 2023
Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks.
Yujin Choi, Jinseong Park, Jaewook Lee, Hoki Kim   +3 more
doaj   +1 more source