Results 1 to 10 of about 217,331 (267)
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
Exploring generative adversarial networks and adversarial training
International Journal of Cognitive Computing in Engineering, 2022 Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from ...
Afia Sajeeda, B M Mainul Hossain, Ph.D
doaj +2 more sources
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
doaj +3 more sources
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
doaj +1 more source
Applied Sciences, 2023 In this paper, we propose an advanced method for adversarial training that focuses on leveraging the underlying structure of adversarial perturbation distributions. Unlike conventional adversarial training techniques that consider adversarial examples in
Bader Rasheed +2 more
doaj +1 more source
IEEE Access, 2022 Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models but are indistinguishable for a human being.
Farzad Nikfam +3 more
doaj +1 more source
Detecting High-Resolution Adversarial Images with Few-Shot Deep Learning
Remote Sensing, 2023 Deep learning models have enabled significant performance improvements to remote sensing image processing. Usually, a large number of training samples is required for detection models.
Junjie Zhao +4 more
doaj +1 more source
Towards Adversarial Robustness for Multi-Mode Data through Metric Learning
Sensors, 2023 Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ...
Sarwar Khan +3 more
doaj +1 more source