Results 111 to 120 of about 1,236,597 (154)

A Security Study of Multimodel Artificial Intelligence System: Adaptive Retention Attack for Object Detection System with Multifocus Image Fusion Model

Advanced Intelligent Systems
Image preprocessing models are usually employed as the preceding operations of high‐level vision tasks to improve the performance. The adversarial attack technology makes both these models face severe challenges.
Xueshuai Gao, Xin Jin, Shengfa Miao, Qian Jiang, Yunyun Dong, Wei Zhou, Shaowen Yao   +6 more
doaj   +1 more source

Traffic adversarial example attack and defense method based on explainable artificial intelligence

Tongxin xuebao
An adversarial example attack method based on XAI was proposed for AI-based NIDS. By identifying critical perturbation features with XAI and applying targeted perturbations while preserving traffic functionality, malicious traffic was gradually altered ...
MA Bowen, GUO Yuanbo, TIAN Jiwei, MA Jun, HU Yongjin   +4 more
doaj  

Rigid Body Adversarial Attacks

2025 International Conference on 3D Vision (3DV)
Due to their performance and simplicity, rigid body simulators are often used in applications where the objects of interest can considered very stiff. However, no material has infinite stiffness, which means there are potentially cases where the non-zero compliance of the seemingly rigid object can cause a significant difference between its ...
Aravind Ramakrishnan, David I. W. Levin, Alec Jacobson   +2 more
openaire   +2 more sources

Breaking and Healing: GAN-Based Adversarial Attacks and Post-Adversarial Recovery for 5G IDSs

IEEE Access
Generative adversarial networks (GANs) have advanced rapidly in data augmentation and generation, and researchers have been exploring their applications in other areas, including adversarial attack generation.
Yasmeen Alslman, Mouhammd Alkasassbeh, Mohammad J. Abdel-Rahman   +2 more
doaj   +1 more source

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

IET Communications
Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully
Fanghao Xu, Chao Wang, Jiakai Liang, Chenyang Zuo, Keqiang Yue, Wenjun Li   +5 more
doaj   +1 more source

Research on adversarial attacks and defense performance of image classification models for automated driving systems

机车电传动
Image classification models have been widely applied to facilitate functions such as autonomous perception and positioning for automated driving in many transportation systems, including automobiles, autonomous rail and urban rail transit systems ...
TANG Jun, HUANG Wenjing, LI Shuang, WU Zili   +3 more
doaj  

Some of the next articles are maybe not open access.

Related searches:

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Malware poses a severe threat to cyber security. Attackers use malware to achieve their malicious purposes, such as unauthorized access, stealing confidential data, blackmailing, etc. Machine learning-based defense methods are applied to classify malware
, , Quan Yu
exaly   +2 more sources

Adversarial Attack Mitigation Strategy for Machine Learning-Based Network Attack Detection Model in Power System

IEEE Transactions on Smart Grid, 2023
The network attack detection model based on machine learning (ML) has received extensive attention and research in PMU measurement data protection of power systems. However, well-trained ML-based detection models are vulnerable to adversarial attacks. By
Yuancheng Li, Rong Huang
exaly   +2 more sources

Average Gradient-Based Adversarial Attack

IEEE Transactions on Multimedia, 2023
Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the classifiers by adding small perturbations to the original example.
Huang Fangjun, , Xianfeng Zhao
exaly   +2 more sources

When Deep Learning-Based Soft Sensors Encounter Reliability Challenges: A Practical Knowledge-Guided Adversarial Attack and Its Defense

IEEE Transactions on Industrial Informatics
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly improved sensing accuracy; however, their vulnerability to adversarial attacks affects their reliability, thus hindering their widespread application. To improve the
Liu Ding, Han Liu, Runyuan Guo
exaly   +2 more sources