Results 61 to 70 of about 527,315 (106)

PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving [PDF]

Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, posing significant risks to the reliability and safety of autonomous ...
Zhang, Wenqiang, Chen, Zhaoyu, Gao, Shuyong, Fu, Jiyuan, Jiang, Kaixun, Guo, Haijing   +5 more
core   +1 more source

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Information
Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted
Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li   +4 more
doaj   +1 more source

Black-Box Universal Adversarial Attack for DNN-Based Models of SAR Automatic Target Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep neural networks (DNNs) are vulnerable to attacks of adversarial examples. Universal adversarial attack algorithms can help evaluate and improve the robustness of the SAR-
Xuanshen Wan, Wei Liu, Chaoyang Niu, Wanjie Lu, Meng Du, Yuanli Li   +5 more
doaj   +1 more source

Research on adversarial attack and defense of photovoltaic power prediction

Dianzi Jishu Yingyong
Deep neural networks have been widely used in photovoltaic power prediction, but they are vulnerable to adversarial attacks. In order to improve the robustness of the prediction model, an adversarial attack algorithm based on fast gradient sign method ...
Zhou Wang
doaj   +1 more source

Adversarial Attacks Against World Models: Hallucination-Driven Policy Failure

Applied Sciences
World models have demonstrated powerful environment modeling capabilities in scenarios such as autonomous driving and robotics, but their adversarial security issues remain underexplored, in particular, adversarial risk analysis of world models.
Junjian Zhang, Hao Tan, Ruonan Li, Aiping Li, Zhaoquan Gu   +4 more
doaj   +1 more source

Attack-Agnostic Adversarial Detection

, 2022
The growing number of adversarial attacks in recent years gives attackers an advantage over defenders, as defenders must train detectors after knowing the types of attacks, and many models need to be maintained to ensure good performance in detecting any
Cheng, Jiaxin, Billa, Jay, Hussein, Mohamed, AbdAlmageed, Wael   +3 more
core  

DLSF:A Textual Adversarial Attack Method Based on Dual-level Semantic Filtering [PDF]

Jisuanji kexue
In the field of commercial applications,deep learning-based text models play a crucial role but are also susceptible to adversarial samples,such as the incorporation of confusing vocabulary into reviews leading to erroneous model responses.A strong ...
XIONG Xi, DING Guangzheng, WANG Juan, ZHANG Shuai
doaj   +1 more source

Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract)

, 2020
Adversarial attack on graph neural network (GNN) is distinctive as it often jointly trains the available nodes to generate a graph as an adversarial example.
Zhao, Jing, Liu, Xiao, Sun, Shiliang
core   +1 more source

Attack-resilient framework for wind power forecasting against civil and adversarial attacks

Attack-resilient framework for wind power forecasting against civil and adversarial ...
MR Islam (9872390), MJ Hossain (7721333), K Akter (20857859), MA Rahman (7722935), MRI Sheikh (14455413)   +4 more
core  

Boosting Adversarial Transferability Through Adversarial Attack Enhancer

Adversarial attacks against deep learning models achieve high performance in white-box settings but often exhibit low transferability in black-box scenarios, especially against defended models.
Hong Huang, Wenli Zeng, Jixin Chen
core   +1 more source