Results 11 to 20 of about 14,798 (256)
Remote Sensing, 2023 Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety ...
Zhen Wang +3 more
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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
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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 +3 more
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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
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Active Learning in Physics: From 101, to Progress, and Perspective
Advanced Quantum Technologies, EarlyView., 2023 In this review, the concept of active learning is introduced to the physicists at the level of beginner without requirement on background in machine learning. It includes most of the latest applications of active learning in branches of physics, covering but not being limited to quantum information, high energy physics, and condensed matter physics. It
Yongcheng Ding +3 more
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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 +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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Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks
IEEE AccessDeep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise ...
Bader Rasheed +4 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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Applied Sciences, 2023 While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that ...
Hiskias Dingeto, Juntae Kim
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