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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 / 1,929 papers shown
Title
Square Attack: a query-efficient black-box adversarial attack via random
  search
Square Attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko
Francesco Croce
Nicolas Flammarion
Matthias Hein
AAML
159
997
0
29 Nov 2019
Attributional Robustness Training using Input-Gradient Spatial Alignment
Attributional Robustness Training using Input-Gradient Spatial Alignment
M. Singh
Nupur Kumari
Puneet Mangla
Abhishek Sinha
V. Balasubramanian
Balaji Krishnamurthy
OOD
98
10
0
29 Nov 2019
Indirect Local Attacks for Context-aware Semantic Segmentation Networks
Indirect Local Attacks for Context-aware Semantic Segmentation Networks
Krishna Kanth Nakka
Mathieu Salzmann
SSegAAML
64
31
0
29 Nov 2019
Can Attention Masks Improve Adversarial Robustness?
Can Attention Masks Improve Adversarial Robustness?
Pratik Vaishnavi
Tianji Cong
Kevin Eykholt
A. Prakash
Amir Rahmati
AAML
142
12
0
27 Nov 2019
Survey of Attacks and Defenses on Edge-Deployed Neural Networks
Survey of Attacks and Defenses on Edge-Deployed Neural Networks
Mihailo Isakov
V. Gadepally
K. Gettings
Michel A. Kinsy
AAML
51
31
0
27 Nov 2019
One Man's Trash is Another Man's Treasure: Resisting Adversarial
  Examples by Adversarial Examples
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples
Chang Xiao
Changxi Zheng
AAML
74
19
0
25 Nov 2019
Invert and Defend: Model-based Approximate Inversion of Generative
  Adversarial Networks for Secure Inference
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
Wei-An Lin
Yogesh Balaji
Pouya Samangouei
Rama Chellappa
61
6
0
23 Nov 2019
Controversial stimuli: pitting neural networks against each other as
  models of human recognition
Controversial stimuli: pitting neural networks against each other as models of human recognition
Tal Golan
Prashant C. Raju
N. Kriegeskorte
AAML
80
39
0
21 Nov 2019
Fine-grained Synthesis of Unrestricted Adversarial Examples
Fine-grained Synthesis of Unrestricted Adversarial Examples
Omid Poursaeed
Tianxing Jiang
Yordanos Goshu
Harry Yang
Serge J. Belongie
Ser-Nam Lim
AAML
115
13
0
20 Nov 2019
Robust Deep Neural Networks Inspired by Fuzzy Logic
Robust Deep Neural Networks Inspired by Fuzzy Logic
Minh Le
OODAAMLAI4CE
118
0
0
20 Nov 2019
Defective Convolutional Networks
Defective Convolutional Networks
Tiange Luo
Tianle Cai
Mengxiao Zhang
Siyu Chen
Di He
Liwei Wang
AAML
51
3
0
19 Nov 2019
Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor
  Attacks in Deep Neural Networks
Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks
Alvin Chan
Yew-Soon Ong
AAML
68
43
0
19 Nov 2019
WITCHcraft: Efficient PGD attacks with random step size
WITCHcraft: Efficient PGD attacks with random step size
Ping Yeh-Chiang
Jonas Geiping
Micah Goldblum
Tom Goldstein
Renkun Ni
Steven Reich
Ali Shafahi
AAML
63
11
0
18 Nov 2019
Smoothed Inference for Adversarially-Trained Models
Smoothed Inference for Adversarially-Trained Models
Yaniv Nemcovsky
Evgenii Zheltonozhskii
Chaim Baskin
Brian Chmiel
Maxim Fishman
A. Bronstein
A. Mendelson
AAMLFedML
53
2
0
17 Nov 2019
Black-Box Adversarial Attack with Transferable Model-based Embedding
Black-Box Adversarial Attack with Transferable Model-based Embedding
Zhichao Huang
Tong Zhang
77
119
0
17 Nov 2019
Defensive Few-shot Learning
Defensive Few-shot Learning
Wenbin Li
Lei Wang
Xingxing Zhang
Lei Qi
Jing Huo
Yang Gao
Jiebo Luo
83
7
0
16 Nov 2019
Adversarial Embedding: A robust and elusive Steganography and
  Watermarking technique
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique
Salah Ghamizi
Maxime Cordy
Mike Papadakis
Yves Le Traon
WIGMAAML
50
7
0
14 Nov 2019
Adversarial Examples in Modern Machine Learning: A Review
Adversarial Examples in Modern Machine Learning: A Review
R. Wiyatno
Anqi Xu
Ousmane Amadou Dia
A. D. Berker
AAML
127
105
0
13 Nov 2019
Robust Design of Deep Neural Networks against Adversarial Attacks based
  on Lyapunov Theory
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Arash Rahnama
A. Nguyen
Edward Raff
AAML
43
20
0
12 Nov 2019
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Ren Pang
Hua Shen
Xinyang Zhang
S. Ji
Yevgeniy Vorobeychik
Xiaopu Luo
Alex Liu
Ting Wang
AAML
64
2
0
05 Nov 2019
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
Guangke Chen
Sen Chen
Lingling Fan
Xiaoning Du
Zhe Zhao
Fu Song
Yang Liu
AAML
114
197
0
03 Nov 2019
MadNet: Using a MAD Optimization for Defending Against Adversarial
  Attacks
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks
Shai Rozenberg
G. Elidan
Ran El-Yaniv
AAML
41
1
0
03 Nov 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble
Enhancing Certifiable Robustness via a Deep Model Ensemble
Huan Zhang
Minhao Cheng
Cho-Jui Hsieh
72
9
0
31 Oct 2019
A Unified Framework for Data Poisoning Attack to Graph-based
  Semi-supervised Learning
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
Xuanqing Liu
Si Si
Xiaojin Zhu
Yang Li
Cho-Jui Hsieh
AAML
96
79
0
30 Oct 2019
Certified Adversarial Robustness for Deep Reinforcement Learning
Certified Adversarial Robustness for Deep Reinforcement Learning
Björn Lütjens
Michael Everett
Jonathan P. How
AAML
107
96
0
28 Oct 2019
Understanding and Quantifying Adversarial Examples Existence in Linear
  Classification
Understanding and Quantifying Adversarial Examples Existence in Linear Classification
Xupeng Shi
A. Ding
AAML
48
3
0
27 Oct 2019
Detection of Adversarial Attacks and Characterization of Adversarial
  Subspace
Detection of Adversarial Attacks and Characterization of Adversarial Subspace
Mohammad Esmaeilpour
P. Cardinal
Alessandro Lameiras Koerich
AAML
54
17
0
26 Oct 2019
Effectiveness of random deep feature selection for securing image
  manipulation detectors against adversarial examples
Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples
Mauro Barni
Ehsan Nowroozi
B. Tondi
Bowen Zhang
AAML
60
17
0
25 Oct 2019
Label Smoothing and Logit Squeezing: A Replacement for Adversarial
  Training?
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Ali Shafahi
Amin Ghiasi
Furong Huang
Tom Goldstein
AAML
74
41
0
25 Oct 2019
Wasserstein Smoothing: Certified Robustness against Wasserstein
  Adversarial Attacks
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
Alexander Levine
Soheil Feizi
AAML
62
61
0
23 Oct 2019
A Useful Taxonomy for Adversarial Robustness of Neural Networks
A Useful Taxonomy for Adversarial Robustness of Neural Networks
L. Smith
AAML
53
6
0
23 Oct 2019
Modeling plate and spring reverberation using a DSP-informed deep neural
  network
Modeling plate and spring reverberation using a DSP-informed deep neural network
M. M. Ramírez
Emmanouil Benetos
Joshua D. Reiss
54
7
0
22 Oct 2019
Adversarial Example Detection by Classification for Deep Speech
  Recognition
Adversarial Example Detection by Classification for Deep Speech Recognition
Saeid Samizade
Zheng-Hua Tan
Chao Shen
X. Guan
AAML
79
35
0
22 Oct 2019
Structure Matters: Towards Generating Transferable Adversarial Images
Structure Matters: Towards Generating Transferable Adversarial Images
Dan Peng
Zizhan Zheng
Linhao Luo
Xiaofeng Zhang
AAML
70
2
0
22 Oct 2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Sven Gowal
J. Uesato
Chongli Qin
Po-Sen Huang
Timothy A. Mann
Pushmeet Kohli
AAML
107
90
0
21 Oct 2019
Are Perceptually-Aligned Gradients a General Property of Robust
  Classifiers?
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?
Simran Kaur
Jeremy M. Cohen
Zachary Chase Lipton
OODAAML
69
66
0
18 Oct 2019
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
Yasaman Esfandiari
Aditya Balu
K. Ebrahimi
Umesh Vaidya
N. Elia
Soumik Sarkar
OOD
59
3
0
18 Oct 2019
Adversarial T-shirt! Evading Person Detectors in A Physical World
Adversarial T-shirt! Evading Person Detectors in A Physical World
Kaidi Xu
Gaoyuan Zhang
Sijia Liu
Quanfu Fan
Mengshu Sun
Hongge Chen
Pin-Yu Chen
Yanzhi Wang
Xue Lin
AAML
90
30
0
18 Oct 2019
Enforcing Linearity in DNN succours Robustness and Adversarial Image
  Generation
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation
A. Sarkar
Nikhil Kumar Gupta
Raghu Sesha Iyengar
AAML
43
11
0
17 Oct 2019
A New Defense Against Adversarial Images: Turning a Weakness into a
  Strength
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Tao Yu
Shengyuan Hu
Chuan Guo
Wei-Lun Chao
Kilian Q. Weinberger
AAML
120
103
0
16 Oct 2019
Extracting robust and accurate features via a robust information
  bottleneck
Extracting robust and accurate features via a robust information bottleneck
Ankit Pensia
Varun Jog
Po-Ling Loh
AAML
78
21
0
15 Oct 2019
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural
  Networks
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Fuyuan Zhang
Sankalan Pal Chowdhury
M. Christakis
AAML
58
8
0
14 Oct 2019
Real-world adversarial attack on MTCNN face detection system
Real-world adversarial attack on MTCNN face detection system
Edgar Kaziakhmedov
Klim Kireev
Grigorii Melnikov
Mikhail Aleksandrovich Pautov
Aleksandr Petiushko
CVBMAAML
73
41
0
14 Oct 2019
Confidence-Calibrated Adversarial Training: Generalizing to Unseen
  Attacks
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz
Matthias Hein
Bernt Schiele
AAML
89
5
0
14 Oct 2019
Man-in-the-Middle Attacks against Machine Learning Classifiers via
  Malicious Generative Models
Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models
Derui Wang
Wang
Chaoran Li
S. Wen
Surya Nepal
Yang Xiang
AAML
34
35
0
14 Oct 2019
Directional Adversarial Training for Cost Sensitive Deep Learning
  Classification Applications
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
M. Terzi
Gian Antonio Susto
Pratik Chaudhari
OODAAML
54
16
0
08 Oct 2019
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation
Guangyu Shen
Chengzhi Mao
Junfeng Yang
Baishakhi Ray
GAN
52
12
0
06 Oct 2019
BUZz: BUffer Zones for defending adversarial examples in image
  classification
BUZz: BUffer Zones for defending adversarial examples in image classification
Kaleel Mahmood
Phuong Ha Nguyen
Lam M. Nguyen
THANH VAN NGUYEN
Marten van Dijk
AAML
62
6
0
03 Oct 2019
Perturbations are not Enough: Generating Adversarial Examples with
  Spatial Distortions
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
He Zhao
Trung Le
Paul Montague
O. Vel
Tamas Abraham
Dinh Q. Phung
AAML
62
8
0
03 Oct 2019
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
Micah Goldblum
Liam H. Fowl
Tom Goldstein
83
13
0
02 Oct 2019
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