Results 1 to 10 of about 34,476 (259)

On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection

Cybersecurity, 2020
Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models.
Yixiang Wang, Shaohua lv, Jiqiang Liu, Xiaolin Chang, Jinqiang Wang   +4 more
doaj   +1 more source

Prostate MR Image Segmentation With Self-Attention Adversarial Training Based on Wasserstein Distance

IEEE Access, 2019
Prostate diseases are very common in men. Accurate segmentation of the prostate plays a significant role in further clinical treatment and diagnosis. There have been some methods that combine the segmentation network and generative adversarial network ...
Chengwei Su, Renxiang Huang, Chang Liu, Tailang Yin, Bo Du   +4 more
doaj   +1 more source

Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors

Mathematics
Adaptive adversarial attacks, where adversaries tailor their strategies with full knowledge of defense mechanisms, pose significant challenges to the robustness of adversarial detectors. In this paper, we introduce RADAR (Robust Adversarial Detection via
Raz Lapid, Almog Dubin, Moshe Sipper
doaj   +1 more source

Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization

Applied Sciences
Physical adversarial attacks face significant challenges in achieving transferability across different object detection models, especially in real-world conditions.
Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli   +2 more
doaj   +1 more source

Developing Hessian–Free Second–Order Adversarial Examples for Adversarial Training

International Journal of Applied Mathematics and Computer Science
Recent studies show that deep neural networks (DNNs) are extremely vulnerable to elaborately designed adversarial examples. Adversarial training, which uses adversarial examples as training data, has been proven to be one of the most effective methods of
Qian Yaguan, Zhang Liangjian, Wang Yuqi, Ji Boyuan, Yao Tengteng, Wang Bin   +5 more
doaj   +1 more source

Defense Architecture for Adversarial Examples of Ensemble Model Traffic Based on FeatureDifference Selection [PDF]

Jisuanji kexue
Currently,anomaly traffic detection models that leverage deep learning technologies are increasingly vulnerable to adversarial example attacks.Adversarial training has emerged as a potent defense mechanism against these adversarial attacks.By ...
HE Yuankang, MA Hailong, HU Tao, JIANG Yiming
doaj   +1 more source

Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security.
Xiaoqing Chen, Ziwei Wang, Dongrui Wu
doaj   +1 more source

MA‐CAT: Misclassification‐Aware Contrastive Adversarial Training

Advanced Intelligent Systems
Vulnerability to adversarial examples poses a significant challenge to the secure application of deep neural networks. Adversarial training and its variants have shown great potential in addressing this problem.
Hongxin Zhi, Hongtao Yu, Shaomei Li, Ruiyang Huang   +3 more
doaj   +1 more source

Adversarial Robustness on Image Classification With k-Means

IEEE Access
Attacks and defences in adversarial machine learning literature have primarily focused on supervised learning. However, it remains an open question whether existing methods and strategies can be adapted to unsupervised learning approaches.
Rollin Omari, Junae Kim, Paul Montague
doaj   +1 more source

Adversarial training improves model interpretability in single-cell RNA-seq analysis. [PDF]

Bioinform Adv, 2023
Sadria M, Layton A, Bader GD.
europepmc   +1 more source