Results 11 to 20 of about 34,476 (259)
Avoiding catastrophic overfitting in fast adversarial training with adaptive similarity step size. [PDF]
PLoS ONEAdversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost.
Jie-Chao Zhao +5 more
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Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation [PDF]
SensorsDespite their high prediction accuracy, deep learning-based soft sensor (DLSS) models face challenges related to adversarial robustness against malicious adversarial attacks, which hinder their widespread deployment and safe application.
Runyuan Guo +3 more
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Adversarial Training Methods for Deep Learning: A Systematic Review
Algorithms, 2022 Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms.
Weimin Zhao +2 more
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STS-AT: A Structured Tensor Flow Adversarial Training Framework for Robust Intrusion Detection [PDF]
SensorsNetwork intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep ...
Juntong Zhu +4 more
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Applied Sciences, 2020 State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks.
Sanglee Park, Jungmin So
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网络与信息安全学报, 2022 Adversarial training is one of the commonly used defense methods against adversarial attacks, by incorporating adversarial samples into the training process.However, the effectiveness of adversarial training heavily relied on the size of the trained ...
Bin WANG, Simin LI, Yaguan QIAN, Jun ZHANG, Chaohao LI, Chenming ZHU, Hongfei ZHANG
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Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition [PDF]
Sensors, 2021 Saeed Ur Rehman +2 more
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Neural Networks, 2023 Adversarial robustness is considered as a required property of deep neural networks. In this study, we discover that adversarially trained models might have significantly different characteristics in terms of margin and smoothness, even they show similar robustness.
Hoki Kim +3 more
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Curriculum Adversarial Training [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Recently, deep learning has been applied to many security-sensitive applications, such as facial authentication. The existence of adversarial examples hinders such applications. The state-of-the-art result on defense shows that adversarial training can be applied to train a robust model on MNIST against adversarial examples; but it fails to achieve a ...
Qi-Zhi Cai, Chang Liu 0021, Dawn Song
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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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