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NAG: Network for Adversary Generation

NAG: Network for Adversary Generation

9 December 2017
Konda Reddy Mopuri
Utkarsh Ojha
Utsav Garg
R. Venkatesh Babu
    AAML
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Papers citing "NAG: Network for Adversary Generation"

26 / 26 papers shown
Title
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization
Yuyao Zhang
Yingzhe Xu
Junyu Shi
L. Zhang
Shengshan Hu
Minghui Li
Yanjun Zhang
AAML
51
1
0
17 Mar 2025
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior
Chanhui Lee
Yeonghwan Song
Jeany Son
AAML
165
0
0
28 Feb 2025
Democratic Training Against Universal Adversarial Perturbations
Bing-Jie Sun
Jun Sun
Wei Zhao
AAML
66
0
0
08 Feb 2025
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
Namhyuk Ahn
Kiyoon Yoo
Wonhyuk Ahn
Daesik Kim
Seung-Hun Nam
AAML
WIGM
DiffM
90
0
0
16 Dec 2024
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
Hao Fang
Jiawei Kong
Wenbo Yu
Bin Chen
Jiawei Li
Hao Wu
Ke Xu
Ke Xu
AAML
VLM
40
13
0
08 Jun 2024
Sparse and Transferable Universal Singular Vectors Attack
Sparse and Transferable Universal Singular Vectors Attack
Kseniia Kuvshinova
Olga Tsymboi
Ivan Oseledets
AAML
38
0
0
25 Jan 2024
AdaptGuard: Defending Against Universal Attacks for Model Adaptation
AdaptGuard: Defending Against Universal Attacks for Model Adaptation
Lijun Sheng
Jian Liang
Ran He
Zilei Wang
Tien-Ping Tan
AAML
51
5
0
19 Mar 2023
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional
  Gradient Estimation
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation
Geunhyeok Yu
Minwoo Jeon
Hyoseok Hwang
AAML
24
1
0
09 Mar 2023
Potential Auto-driving Threat: Universal Rain-removal Attack
Potential Auto-driving Threat: Universal Rain-removal Attack
Jincheng Hu
Jihao Li
Zhuoran Hou
Jingjing Jiang
Cunjia Liu
Yuanjian Zhang
AAML
24
4
0
18 Nov 2022
Towards Understanding and Boosting Adversarial Transferability from a
  Distribution Perspective
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Yao Zhu
YueFeng Chen
Xiaodan Li
Kejiang Chen
Yuan He
Xiang Tian
Bo Zheng
Yao-wu Chen
Qingming Huang
AAML
33
58
0
09 Oct 2022
An Efficient Method for Sample Adversarial Perturbations against
  Nonlinear Support Vector Machines
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines
Wen Su
Qingna Li
AAML
13
0
0
12 Jun 2022
Generative Dynamic Patch Attack
Generative Dynamic Patch Attack
Xiang Li
Shihao Ji
AAML
27
22
0
08 Nov 2021
ADC: Adversarial attacks against object Detection that evade Context
  consistency checks
ADC: Adversarial attacks against object Detection that evade Context consistency checks
Mingjun Yin
Shasha Li
Chengyu Song
M. Salman Asif
A. Roy-Chowdhury
S. Krishnamurthy
AAML
27
22
0
24 Oct 2021
Robust Feature-Level Adversaries are Interpretability Tools
Robust Feature-Level Adversaries are Interpretability Tools
Stephen Casper
Max Nadeau
Dylan Hadfield-Menell
Gabriel Kreiman
AAML
48
27
0
07 Oct 2021
Improving Adversarial Robustness for Free with Snapshot Ensemble
Improving Adversarial Robustness for Free with Snapshot Ensemble
Yihao Wang
AAML
UQCV
17
1
0
07 Oct 2021
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to
  CNNs
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Philipp Benz
Soomin Ham
Chaoning Zhang
Adil Karjauv
In So Kweon
AAML
ViT
47
78
0
06 Oct 2021
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese
  Visual Tracking
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
Zhenbang Li
Yaya Shi
Jin Gao
Shaoru Wang
Bing Li
Pengpeng Liang
Weiming Hu
AAML
39
26
0
06 May 2021
A Survey On Universal Adversarial Attack
A Survey On Universal Adversarial Attack
Chaoning Zhang
Philipp Benz
Chenguo Lin
Adil Karjauv
Jing Wu
In So Kweon
AAML
23
90
0
02 Mar 2021
Locally optimal detection of stochastic targeted universal adversarial
  perturbations
Locally optimal detection of stochastic targeted universal adversarial perturbations
Amish Goel
P. Moulin
AAML
19
2
0
08 Dec 2020
Transferable Universal Adversarial Perturbations Using Generative Models
Transferable Universal Adversarial Perturbations Using Generative Models
Atiyeh Hashemi
Andreas Bär
S. Mozaffari
Tim Fingscheidt
AAML
27
17
0
28 Oct 2020
Universal Adversarial Perturbations: A Survey
Universal Adversarial Perturbations: A Survey
Ashutosh Chaubey
Nikhil Agrawal
Kavya Barnwal
K. K. Guliani
Pramod Mehta
OOD
AAML
36
46
0
16 May 2020
A Little Fog for a Large Turn
A Little Fog for a Large Turn
Harshitha Machiraju
V. Balasubramanian
AAML
15
9
0
16 Jan 2020
Simple iterative method for generating targeted universal adversarial
  perturbations
Simple iterative method for generating targeted universal adversarial perturbations
Hokuto Hirano
Kazuhiro Takemoto
AAML
27
30
0
15 Nov 2019
Defending Against Universal Perturbations With Shared Adversarial
  Training
Defending Against Universal Perturbations With Shared Adversarial Training
Chaithanya Kumar Mummadi
Thomas Brox
J. H. Metzen
AAML
18
60
0
10 Dec 2018
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep
  Convolutional Networks
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Kenneth T. Co
Luis Muñoz-González
Sixte de Maupeou
Emil C. Lupu
AAML
22
67
0
30 Sep 2018
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
293
3,112
0
04 Nov 2016
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