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Perturbation Towards Easy Samples Improves Targeted Adversarial
  Transferability

Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability

8 June 2024
Junqi Gao
Biqing Qi
Yao Li
Zhichang Guo
Dong Li
Yuming Xing
Dazhi Zhang
    AAML
ArXivPDFHTML

Papers citing "Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability"

3 / 3 papers shown
Title
On the Convergence and Robustness of Adversarial Training
On the Convergence and Robustness of Adversarial Training
Yisen Wang
Xingjun Ma
James Bailey
Jinfeng Yi
Bowen Zhou
Quanquan Gu
AAML
192
345
0
15 Dec 2021
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
L. V. D. van der Maaten
Kilian Q. Weinberger
PINN
3DV
247
36,356
0
25 Aug 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
119
1,198
0
16 Aug 2016
1