Adversarial attacks and defenses on graphs
Adversarial Attacks and Defenses on Graphs Page 1 Adversarial Attacks and Defenses on
Graphs: A Review, A Tool and Empirical Studies Wei Jin†, Yaxin Li†, Han Xu†, Yiqi Wang† …
Graphs: A Review, A Tool and Empirical Studies Wei Jin†, Yaxin Li†, Han Xu†, Yiqi Wang† …
[PDF][PDF] Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang - cse.msu.edu
Deep neural networks (DNNs) have achieved significant performance in various tasks.
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
[PDF][PDF] Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang - kdd.org
Deep neural networks (DNNs) have achieved significant performance in various tasks.
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal… - arXiv e …, 2020 - ui.adsabs.harvard.edu
Deep neural networks (DNNs) have achieved significant performance in various tasks.
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal… - arXiv preprint arXiv …, 2020 - arxiv.org
Deep neural networks (DNNs) have achieved significant performance in various tasks.
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
[PDF][PDF] Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang - kdd.org
Deep neural networks (DNNs) have achieved significant performance in various tasks.
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
However, recent studies have shown that DNNs can be easily fooled by small perturbation …
