Results 31 to 40 of about 1,185,392 (332)
Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review
Applied Computer Systems, 2021 In recent years, various domains have been influenced by the rapid growth of machine learning. Autonomous driving is an area that has tremendously developed in parallel with the advancement of machine learning.
Mahima K. T. Y. +2 more
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EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 more
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Adversarial Machine Learning for 5G Communications Security [PDF]
Game Theory and Machine Learning for Cyber Security, 2021 Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization.
Y. Sagduyu, T. Erpek, Yi Shi
semanticscholar +1 more source
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Anomaly detection in videos is an important computer vision problem with various applications including auto-mated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on ...
Furkan Mumcu, Keval Doshi, Yasin Yılmaz
semanticscholar +1 more source
Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective [PDF]
ACM Computing Surveys, 2020 Deep Learning algorithms have achieved state-of-the-art performance for Image Classification. For this reason, they have been used even in security-critical applications, such as biometric recognition systems and self-driving cars.
G. R. Machado +2 more
semanticscholar +1 more source
Adversarial Machine Learning [PDF]
IEEE Internet Computing, 2011 The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near ...
openaire +1 more source
Impact of adversarial examples on deep learning models for biomedical image segmentation [PDF]
, 2019 Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples.
C Pena-Betancor +3 more
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Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case [PDF]
International Black Sea Conference on Communications and Networking, 2021 6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems.
Evren Çatak +2 more
semanticscholar +1 more source
Adversarial Attacks and Defenses in Deep Learning
Engineering, 2020 With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren +3 more
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Research on filter-based adversarial feature selection against evasion attacks
Dianxin kexue, 2023 With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are
Qimeng HUANG, Miaomiao WU, Yun LI
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