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An Alternative Surrogate Loss for PGD-based Adversarial Testing

An Alternative Surrogate Loss for PGD-based Adversarial Testing

21 October 2019
Sven Gowal
J. Uesato
Chongli Qin
Po-Sen Huang
Timothy A. Mann
Pushmeet Kohli
    AAML
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Papers citing "An Alternative Surrogate Loss for PGD-based Adversarial Testing"

23 / 23 papers shown
Title
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
Ping Guo
Cheng Gong
Xi Lin
Fei Liu
Zhichao Lu
Qingfu Zhang
Zhenkun Wang
AAML
45
0
0
13 Jan 2025
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Yong Xie
Weijie Zheng
Hanxun Huang
Guangnan Ye
Xingjun Ma
AAML
72
1
0
20 Nov 2024
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Binghui Li
Yuanzhi Li
OOD
33
2
0
11 Oct 2024
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive
  Smoothing
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
Yatong Bai
Brendon G. Anderson
Aerin Kim
Somayeh Sojoudi
AAML
36
18
0
29 Jan 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks
Nikolaos Antoniou
Efthymios Georgiou
Alexandros Potamianos
AAML
29
5
0
15 Dec 2022
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph
  Consistency
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency
Hassan Mahmood
Ehsan Elhamifar
AAML
21
0
0
11 Jul 2022
Investigating Top-$k$ White-Box and Transferable Black-box Attack
Investigating Top-kkk White-Box and Transferable Black-box Attack
Chaoning Zhang
Philipp Benz
Adil Karjauv
Jae-Won Cho
Kang Zhang
In So Kweon
31
42
0
30 Mar 2022
Constrained Gradient Descent: A Powerful and Principled Evasion Attack
  Against Neural Networks
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
Weiran Lin
Keane Lucas
Lujo Bauer
Michael K. Reiter
Mahmood Sharif
AAML
31
5
0
28 Dec 2021
Data Augmentation Can Improve Robustness
Data Augmentation Can Improve Robustness
Sylvestre-Alvise Rebuffi
Sven Gowal
D. A. Calian
Florian Stimberg
Olivia Wiles
Timothy A. Mann
AAML
22
270
0
09 Nov 2021
Improving Robustness using Generated Data
Improving Robustness using Generated Data
Sven Gowal
Sylvestre-Alvise Rebuffi
Olivia Wiles
Florian Stimberg
D. A. Calian
Timothy A. Mann
36
293
0
18 Oct 2021
Adversarial Examples Make Strong Poisons
Adversarial Examples Make Strong Poisons
Liam H. Fowl
Micah Goldblum
Ping Yeh-Chiang
Jonas Geiping
Wojtek Czaja
Tom Goldstein
SILM
32
132
0
21 Jun 2021
LAFEAT: Piercing Through Adversarial Defenses with Latent Features
LAFEAT: Piercing Through Adversarial Defenses with Latent Features
Yunrui Yu
Xitong Gao
Chengzhong Xu
AAML
FedML
33
44
0
19 Apr 2021
Fixing Data Augmentation to Improve Adversarial Robustness
Fixing Data Augmentation to Improve Adversarial Robustness
Sylvestre-Alvise Rebuffi
Sven Gowal
D. A. Calian
Florian Stimberg
Olivia Wiles
Timothy A. Mann
AAML
36
269
0
02 Mar 2021
A Multiclass Boosting Framework for Achieving Fast and Provable
  Adversarial Robustness
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness
Jacob D. Abernethy
Pranjal Awasthi
Satyen Kale
AAML
27
6
0
01 Mar 2021
Composite Adversarial Attacks
Composite Adversarial Attacks
Xiaofeng Mao
YueFeng Chen
Shuhui Wang
Hang Su
Yuan He
Hui Xue
AAML
33
48
0
10 Dec 2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial
  Defenses
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Gaurang Sriramanan
Sravanti Addepalli
Arya Baburaj
R. Venkatesh Babu
AAML
25
92
0
30 Nov 2020
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
234
678
0
19 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded
  Adversarial Examples
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal
Chongli Qin
J. Uesato
Timothy A. Mann
Pushmeet Kohli
AAML
17
324
0
07 Oct 2020
Label Smoothing and Adversarial Robustness
Label Smoothing and Adversarial Robustness
Chaohao Fu
Hongbin Chen
Na Ruan
Weijia Jia
AAML
10
12
0
17 Sep 2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural
  Networks
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi
Natalie Frank
M. Mohri
AAML
34
56
0
28 Apr 2020
On Adaptive Attacks to Adversarial Example Defenses
On Adaptive Attacks to Adversarial Example Defenses
Florian Tramèr
Nicholas Carlini
Wieland Brendel
A. Madry
AAML
104
820
0
19 Feb 2020
Towards Sharper First-Order Adversary with Quantized Gradients
Towards Sharper First-Order Adversary with Quantized Gradients
Zhuanghua Liu
Ivor W. Tsang
AAML
19
0
0
01 Feb 2020
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
287
5,842
0
08 Jul 2016
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