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Obfuscated Gradients Give a False Sense of Security: Circumventing
  Defenses to Adversarial Examples
v1v2v3v4 (latest)

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1 February 2018
Anish Athalye
Nicholas Carlini
D. Wagner
    AAML
ArXiv (abs)PDFHTML

Papers citing "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples"

50 / 1,983 papers shown
Building Robust Ensembles via Margin Boosting
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Hongyang R. Zhang
Aaron Courville
Yoshua Bengio
Pradeep Ravikumar
A. Suggala
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Certified Robustness in Federated Learning
Certified Robustness in Federated Learning
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Juan C. Pérez
Egor Shulgin
Peter Richtárik
Guohao Li
AAMLFedML
262
10
0
06 Jun 2022
Towards Evading the Limits of Randomized Smoothing: A Theoretical
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Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis
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Alexandre Araujo
Rafael Pinot
Y. Chevaleyre
Jamal Atif
AAML
168
3
0
03 Jun 2022
Gradient Obfuscation Checklist Test Gives a False Sense of Security
Gradient Obfuscation Checklist Test Gives a False Sense of Security
Nikola Popovic
D. Paudel
Thomas Probst
Luc Van Gool
AAML
190
6
0
03 Jun 2022
FACM: Intermediate Layer Still Retain Effective Features against
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FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples
Xiangyuan Yang
Jie Lin
Hanlin Zhang
Xinyu Yang
Peng Zhao
AAML
249
0
0
02 Jun 2022
Why Adversarial Training of ReLU Networks Is Difficult?
Why Adversarial Training of ReLU Networks Is Difficult?
Feng He
Hao Zhang
Yue Xin
Wen Shen
Jie Ren
Quanshi Zhang
AAML
146
6
0
30 May 2022
Guided Diffusion Model for Adversarial Purification
Guided Diffusion Model for Adversarial Purification
Jinyi Wang
Zhaoyang Lyu
Dahua Lin
Bo Dai
Hongfei Fu
DiffM
536
114
0
30 May 2022
Robust Weight Perturbation for Adversarial Training
Robust Weight Perturbation for Adversarial TrainingInternational Joint Conference on Artificial Intelligence (IJCAI), 2022
Chaojian Yu
Bo Han
Biwei Huang
Li Shen
Shiming Ge
Bo Du
Tongliang Liu
AAML
174
42
0
30 May 2022
Superclass Adversarial Attack
Superclass Adversarial Attack
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Hiroshi Kera
T. Yamasaki
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210
1
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29 May 2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and
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Lei Cheng
Feng Yin
Sergios Theodoridis
S. Chatzis
Tsung-Hui Chang
299
91
0
28 May 2022
Certified Robustness Against Natural Language Attacks by Causal
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Certified Robustness Against Natural Language Attacks by Causal InterventionInternational Conference on Machine Learning (ICML), 2022
Haiteng Zhao
Chang Ma
Xinshuai Dong
Anh Tuan Luu
Zhi-Hong Deng
Hanwang Zhang
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361
42
0
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EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density
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Mitch Hill
Jonathan Mitchell
Chu Chen
Yuan Du
M. Shah
Song-Chun Zhu
200
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24 May 2022
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box
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Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksNeural Information Processing Systems (NeurIPS), 2022
Sizhe Chen
Zhehao Huang
Qinghua Tao
Yingwen Wu
Cihang Xie
Xiaolin Huang
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401
31
0
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Alleviating Robust Overfitting of Adversarial Training With Consistency
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Shudong Zhang
Haichang Gao
Tianwei Zhang
Yunyi Zhou
Zihui Wu
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202
6
0
24 May 2022
Post-breach Recovery: Protection against White-box Adversarial Examples
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Post-breach Recovery: Protection against White-box Adversarial Examples for Leaked DNN ModelsConference on Computer and Communications Security (CCS), 2022
Shawn Shan
Wen-Luan Ding
Emily Wenger
Haitao Zheng
Ben Y. Zhao
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217
15
0
21 May 2022
Improving Robustness against Real-World and Worst-Case Distribution
  Shifts through Decision Region Quantification
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region QuantificationInternational Conference on Machine Learning (ICML), 2022
Leo Schwinn
Leon Bungert
A. Nguyen
René Raab
Falk Pulsmeyer
Doina Precup
Björn Eskofier
Dario Zanca
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166
19
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On Trace of PGD-Like Adversarial Attacks
On Trace of PGD-Like Adversarial AttacksInternational Conference on Pattern Recognition (ICPR), 2022
Mo Zhou
Vishal M. Patel
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293
5
0
19 May 2022
Empirical Advocacy of Bio-inspired Models for Robust Image Recognition
Empirical Advocacy of Bio-inspired Models for Robust Image Recognition
Harshitha Machiraju
Oh-hyeon Choung
Michael H. Herzog
P. Frossard
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182
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Lessons Learned: Defending Against Property Inference Attacks
Lessons Learned: Defending Against Property Inference Attacks
Joshua Stock
Jens Wettlaufer
Daniel Demmler
Hannes Federrath
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279
1
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18 May 2022
Robust Representation via Dynamic Feature Aggregation
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Haozhe Liu
Haoqin Ji
Yuexiang Li
Nanjun He
Haoqian Wu
Feng Liu
Linlin Shen
Yefeng Zheng
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236
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Diffusion Models for Adversarial Purification
Diffusion Models for Adversarial PurificationInternational Conference on Machine Learning (ICML), 2022
Weili Nie
Brandon Guo
Yujia Huang
Chaowei Xiao
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Anima Anandkumar
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499
592
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AEON: A Method for Automatic Evaluation of NLP Test Cases
AEON: A Method for Automatic Evaluation of NLP Test CasesInternational Symposium on Software Testing and Analysis (ISSTA), 2022
Shu Yang
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Pinjia He
Yuxin Su
Michael R. Lyu
186
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Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
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Ameya Joshi
Minh Pham
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J. Zico Kolter
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UQCVAAML
219
2
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12 May 2022
Do You Think You Can Hold Me? The Real Challenge of Problem-Space
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Chen Hajaj
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219
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Subverting Fair Image Search with Generative Adversarial Perturbations
Subverting Fair Image Search with Generative Adversarial PerturbationsConference on Fairness, Accountability and Transparency (FAccT), 2022
A. Ghosh
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Chris L. Wilson
346
8
0
05 May 2022
CE-based white-box adversarial attacks will not work using super-fitting
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Lei Sun
Leyu Dai
Song Guo
Xiuqing Mao
Xiaoqin Wang
Bayi Xu
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279
0
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Enhancing Adversarial Training with Feature Separability
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Yaxin Li
Xiaorui Liu
Han Xu
Wentao Wang
Shucheng Zhou
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131
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DDDM: a Brain-Inspired Framework for Robust Classification
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Xiyuan Chen
Xingyu Li
Yi Zhou
Tianming Yang
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135
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01 May 2022
Detecting Textual Adversarial Examples Based on Distributional
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Na Liu
Mark Dras
Wei Emma Zhang
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134
6
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29 Apr 2022
Defending Person Detection Against Adversarial Patch Attack by using
  Universal Defensive Frame
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive FrameIEEE Transactions on Image Processing (IEEE TIP), 2022
Youngjoon Yu
Hong Joo Lee
Hakmin Lee
Yong Man Ro
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153
18
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When adversarial examples are excusable
When adversarial examples are excusable
Pieter-Jan Kindermans
Charles Staats
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111
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How Sampling Impacts the Robustness of Stochastic Neural Networks
How Sampling Impacts the Robustness of Stochastic Neural NetworksNeural Information Processing Systems (NeurIPS), 2022
Sina Daubener
Asja Fischer
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238
1
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Case-Aware Adversarial Training
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Yang Liu
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Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile
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258
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Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot
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252
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Liuer Mihou: A Practical Framework for Generating and Evaluating
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153
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Toward Robust Spiking Neural Network Against Adversarial Perturbation
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Ling Liang
Kaidi Xu
Xing Hu
Lei Deng
Yuan Xie
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163
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84
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123
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