Results 261 to 270 of about 804,777 (293)
Some of the next articles are maybe not open access.

Adversarial Attacks and Defenses

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002   +3 more
openaire   +1 more source

DeepRobust: a Platform for Adversarial Attacks and Defenses

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization.
Yaxin Li 0001   +3 more
openaire   +2 more sources

MTD-AD: Moving Target Defense as Adversarial Defense

IEEE Transactions on Dependable and Secure Computing
Network Intrusion Detection Systems (NIDSes) are increasingly incorporating Machine Learning (ML) and Deep Learning (DL) algorithms for detecting network intrusions. However, ML/DL algorithms are susceptible to adversarial examples, which can lead to the misclassification of input data.
Ke He   +2 more
openaire   +5 more sources

Variational Adversarial Defense: A Bayes Perspective for Adversarial Training

IEEE Transactions on Pattern Analysis and Machine Intelligence
Various methods have been proposed to defend against adversarial attacks. However, there is a lack of enough theoretical guarantee of the performance, thus leading to two problems: First, deficiency of necessary adversarial training samples might attenuate the normal gradient's back-propagation, which leads to overfitting and gradient masking ...
Chenglong Zhao   +5 more
openaire   +3 more sources

Adversarial Defense in Aerial Detection

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
Yuwei Chen, Shiyong Chu
openaire   +1 more source

Causal Disentanglement for Adversarial Defense

2023
Ji-Young Park   +3 more
openaire   +2 more sources

Attack-less adversarial training for a robust adversarial defense

Applied Intelligence, 2021
Jiacang Ho, Byung-Gook Lee, Dae-Ki Kang
openaire   +2 more sources

Home - About - Disclaimer - Privacy