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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Active machine learning approach to adversarial training improves trade-off between natural accuracy and adversarial robustness [PDF]
Scientific ReportsUnderstanding our world which is open and diverse requires foundation models that generalize well while trustworthy. Adversarial training has been considered to be one of the most effective strategies to achieve robust learning systems, yet adversarial ...
Seyed Mohammad Hadi Mirsadeghi
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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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Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training
TechnologiesThe adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth
Anna Chistyakova +6 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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Evaluation of GAN-Based Model for Adversarial Training [PDF]
Sensors, 2023 Qusay H Mahmoud, Weimin Zhao
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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 Karrar Hameed Abdulkareem +2 more
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