Results 31 to 40 of about 6,306,959 (200)

Adversarial Metric Learning [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results when the test data is similar to the training data. However, the trained models often fail to produce reliable distances on the ambiguous test pairs due to the different ...
Shuo Chen 0003, Chen Gong 0002, Jian Yang 0003, Xiang Li 0041, Yang Wei 0003, Jun Li 0027   +5 more
openaire   +2 more sources

Adversarial learning for counterfactual fairness

Machine Learning, 2022
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which ensure fairness at the most individual level. Rather than globally considering equity over the entire population, the idea is to imagine what any individual would look like ...
Grari, Vincent, Lamprier, Sylvain, Detyniecki, Marcin   +2 more
openaire   +4 more sources

Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT [PDF]

, 2021
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan, Buchanan, William J., Alexios Mylonas, Mylonas, Alexios, Pavlos Papadopoulos, Pitropakis, Nikolaos, Thornewill Von Essen, Oliver, Papadopoulos, Pavlos, Chrysoulas, Christos, Essen, Oliver Thornewill von, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Chrysoulas, Christos; id_orcid   +13 more
core   +1 more source

Adversarial Continual Learning [PDF]

, 2020
Accepted at ECCV ...
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, Marcus Rohrbach   +4 more
openaire   +4 more sources

EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT

Heliyon, 2023
The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li, Jingxuan Wang, Yuhe Wang, Guohui Zhou, Yan Zhao   +4 more
doaj   +1 more source

Survey on adversarial attacks and defense of face forgery and detection

网络与信息安全学报, 2023
Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and ...
Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO
doaj   +3 more sources

Adversarial Label Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
We consider the task of training classifiers without labels. We propose a weakly supervised method—adversarial label learning—that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose.
Chidubem Arachie, Bert Huang
openaire   +4 more sources

Adversarial Deep Learning Models with Multiple Adversaries [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2019
We develop an adversarial learning algorithm for supervised classification in general and Convolutional Neural Networks (CNN) in particular. The algorithm's objective is to produce small changes to the data distribution defined over positive and negative class labels so that the resulting data distribution is misclassified by the CNN.
Aneesh Sreevallabh Chivukula, Wei Liu 0007   +1 more
openaire   +1 more source

“Adversarial Examples” for Proof-of-Learning

2022 IEEE Symposium on Security and Privacy (SP), 2022
To appear in the 43rd IEEE Symposium on Security and ...
Rui Zhang 0118, Jian Liu 0012, Yuan Ding, Zhibo Wang 0001, Qingbiao Wu, Kui Ren 0001   +5 more
openaire   +4 more sources

Learning with a Strong Adversary

CoRR, 2015
The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, \emph{learning with a strong adversary}, that learns robust classifiers from supervised data. The proposed method takes finding adversarial examples as an intermediate step.
Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvári   +3 more
openaire   +2 more sources