Results 271 to 280 of about 148,046 (305)

Cross-Species Behavioral Representation Learning Using Domain-Adversarial Adaptation on Wearable IMU Signals. [PDF]

Biomimetics (Basel)
Acı Çİ, Say F, Zaimoğlu EA.
europepmc   +1 more source

SecureTrust-FL: trust-aware privacy-preserving federated learning for network intrusion detection. [PDF]

Sci Rep
Alshammari NS, Mishra S, Rathi M, Goel N, Tahzib S.   +4 more
europepmc   +1 more source

Empirical analysis of adversarial robustness in 3D Gaussian Splatting under multi-view inconsistency attacks. [PDF]

Sci Rep
Kwon H, Baek JW.
europepmc   +1 more source

Adversarial symmetric GANs: Bridging adversarial samples and adversarial networks [PDF]

Neural Networks, 2021
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that ...
Rong Zhao, Guoqi Li, Faqiang Liu
exaly   +5 more sources

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Adversarial Training With Anti-Adversaries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adversarial training is effective in improving the robustness of deep neural networks. However, existing studies still exhibit significant drawbacks in terms of the robustness, generalization, and fairness of models. In this study, we validate the importance of different perturbation directions (i.e., adversarial and anti-adversarial) and bounds from ...
Xiaoling Zhou, Ou Wu 0001, Nan Yang
openaire   +3 more sources

Adversarial learning

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2].
Daniel Lowd, Christopher Meek
openaire   +1 more source

Learning Universal Adversarial Perturbation by Adversarial Example

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li, Yanhua Yang, Kun Wei, Xu Yang 0019, Heng Huang 0001   +4 more
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Adversary of the Queen’s Adversaries

1967
The war in the Netherlands was more effective in destroying the reputation of Leicester than that of the Spanish army. The Earl’s assumption in January 1586 of the governorship, which implied an English claim to sovereignty, enraged Elizabeth. Her object was to restore the liberties of the Netherlands but maintain the nominal suzerainty of Spain ...
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Adversarial and counter-adversarial support vector machines

Neurocomputing, 2019
Abstract A support vector machine (SVM) is a simple but yet powerful classification technique widely used in various applications, such as handwritten digits classification and face recognition. However, as any linear classification algorithm, it is vulnerable to adversarial attacks on test/training data.
Ihor Indyk, Michael Zabarankin
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