Quantum adversarial machine learning [PDF]
Physical Review Research, 2020 Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
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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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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
IEEE Access, 2018 Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security.
Naveed Akhtar, Ajmal Mian
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Survey of Adversarial Attacks and Defense Methods for Deep Learning Model [PDF]
Jisuanji gongcheng, 2021 As an important part of artificial intelligence technology,deep learning is widely used in computer vision,natural language processing and other fields.Although deep learning performs well in tasks such as image classification and target detection,its ...
JIANG Yan, ZHANG Liguo
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Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode Recognition
, 2022 This chapter demonstrates how adversarial learning can be used in the mobile computing domain. Specifically, we address the problem of improving the recognition of human activities from smartphone sensors, when limited training data is available ...
Wang, L +4 more
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Learning new attack vectors from misuse cases with deep reinforcement learning
Frontiers in Energy Research, 2023 Modern smart grids already consist of various components that interleave classical Operational Technology (OT) with Information and Communication Technology (ICT), which, in turn, have opened the power grid to advanced approaches using distributed ...
Eric M. S. P. Veith +2 more
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Adversarial attacks on deep learning models in smart grids
Energy Reports, 2022 A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
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Adversarial Self-Supervised Learning for Robust SAR Target Recognition
Remote Sensing, 2021 Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years.
Yanjie Xu +5 more
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Adversarial attacks against supervised machine learning based network intrusion detection systems.
PLoS ONE, 2022 Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani +3 more
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Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification [PDF]
, 2023 Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks.
Panagiotis Andriotis +8 more
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