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1706.04701
Cited By
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
15 June 2017
Warren He
James Wei
Xinyun Chen
Nicholas Carlini
Basel Alomair
AAML
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Papers citing
"Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong"
50 / 126 papers shown
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysis
Pankaj Deoli
Rohit Kumar
A. Vierling
Karsten Berns
466
4
0
03 Feb 2024
Refutation of Shapley Values for XAI -- Additional Evidence
Xuanxiang Huang
Sasha Rubin
AAML
383
4
0
30 Sep 2023
Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection
IEEE Transactions on Information Forensics and Security (IEEE TIFS), 2023
Hao Fu
Prashanth Krishnamurthy
S. Garg
Farshad Khorrami
AAML
262
15
0
11 Jul 2023
Computational Asymmetries in Robust Classification
International Conference on Machine Learning (ICML), 2023
Samuele Marro
M. Lombardi
AAML
190
2
0
25 Jun 2023
Detection of Adversarial Physical Attacks in Time-Series Image Data
Ramneet Kaur
Y. Kantaros
Wenwen Si
James Weimer
Insup Lee
AAML
208
3
0
27 Apr 2023
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes
Thomas Philippon
Christian Gagné
AAML
195
1
0
04 Mar 2023
Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection
IEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2023
Aqib Rashid
Jose Such
AAML
201
5
0
01 Feb 2023
Adversarial Detection by Approximation of Ensemble Boundary
Neurocomputing (Neurocomputing), 2022
T. Windeatt
AAML
797
0
0
18 Nov 2022
Robust Few-shot Learning Without Using any Adversarial Samples
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Gaurav Kumar Nayak
Ruchit Rawal
Inder Khatri
Anirban Chakraborty
AAML
151
5
0
03 Nov 2022
Data-free Defense of Black Box Models Against Adversarial Attacks
Gaurav Kumar Nayak
Inder Khatri
Ruchit Rawal
Anirban Chakraborty
AAML
239
2
0
03 Nov 2022
Ares: A System-Oriented Wargame Framework for Adversarial ML
Farhan Ahmed
Pratik Vaishnavi
Kevin Eykholt
Amir Rahmati
AAML
225
8
0
24 Oct 2022
Hindering Adversarial Attacks with Implicit Neural Representations
International Conference on Machine Learning (ICML), 2022
Andrei A. Rusu
D. A. Calian
Sven Gowal
R. Hadsell
AAML
427
5
0
22 Oct 2022
A Perturbation Resistant Transformation and Classification System for Deep Neural Networks
Nathaniel R. Dean
D. Sarkar
AAML
153
0
0
25 Aug 2022
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy
International Conference on Machine Learning (ICML), 2022
R. Olivier
Bhiksha Raj
AAML
234
10
0
08 Jul 2022
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection
Abderrahmen Amich
Ata Kaboudi
Birhanu Eshete
AAML
OODD
123
3
0
15 Jun 2022
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge
European Conference/Workshop on Wireless Sensor Networks (EWSN), 2022
Qun Song
Zhenyu Yan
W. Luo
Rui Tan
AAML
316
5
0
18 Apr 2022
On the benefits of knowledge distillation for adversarial robustness
Javier Maroto
Guillermo Ortiz-Jiménez
P. Frossard
AAML
FedML
300
28
0
14 Mar 2022
Enhancing Adversarial Robustness for Deep Metric Learning
Computer Vision and Pattern Recognition (CVPR), 2022
Mo Zhou
Vishal M. Patel
AAML
238
19
0
02 Mar 2022
Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem
Abderrahmen Amich
Birhanu Eshete
OOD
207
4
0
18 Feb 2022
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
Network and Distributed System Security Symposium (NDSS), 2022
Yijun Yang
Ruiyuan Gao
Yu Li
Qiuxia Lai
Qiang Xu
GAN
AAML
285
26
0
24 Jan 2022
The Security of Deep Learning Defences for Medical Imaging
Mosh Levy
Guy Amit
Yuval Elovici
Yisroel Mirsky
AAML
MedIm
298
11
0
21 Jan 2022
Frequency Centric Defense Mechanisms against Adversarial Examples
Sanket B. Shah
Param Raval
Harin Khakhi
M. Raval
AAML
248
7
0
26 Oct 2021
Tensor Normalization and Full Distribution Training
Wolfgang Fuhl
OOD
321
5
0
06 Sep 2021
On the Importance of Encrypting Deep Features
Xingyang Ni
H. Huttunen
Esa Rahtu
MIACV
192
0
0
16 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
International Conference on Machine Learning (ICML), 2021
Florian Tramèr
AAML
361
82
0
24 Jul 2021
Controlled Caption Generation for Images Through Adversarial Attacks
Nayyer Aafaq
Naveed Akhtar
Wei Liu
M. Shah
Lin Wang
AAML
175
13
0
07 Jul 2021
Who is Responsible for Adversarial Defense?
Kishor Datta Gupta
D. Dasgupta
AAML
152
2
0
27 Jun 2021
DeepMoM: Robust Deep Learning With Median-of-Means
Journal of Computational And Graphical Statistics (JCGS), 2021
Shih-Ting Huang
Johannes Lederer
FedML
302
8
0
28 May 2021
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
Ruoxi Qin
Linyuan Wang
Xing-yuan Chen
Xuehui Du
Bin Yan
AAML
166
6
0
06 May 2021
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021
Luke Chang
Katharina Dost
Kaiqi Zhao
Ambra Demontis
Fabio Roli
Gillian Dobbie
Jörg Simon Wicker
AAML
323
2
0
02 May 2021
On the robustness of randomized classifiers to adversarial examples
Machine-mediated learning (ML), 2021
Rafael Pinot
Laurent Meunier
Florian Yger
Cédric Gouy-Pailler
Y. Chevaleyre
Jamal Atif
AAML
191
15
0
22 Feb 2021
Security and Privacy for Artificial Intelligence: Opportunities and Challenges
Ayodeji Oseni
Nour Moustafa
Helge Janicke
Peng Liu
Z. Tari
A. Vasilakos
AAML
217
70
0
09 Feb 2021
Amata: An Annealing Mechanism for Adversarial Training Acceleration
AAAI Conference on Artificial Intelligence (AAAI), 2019
Nanyang Ye
Qianxiao Li
Xiao-Yun Zhou
Zhanxing Zhu
AAML
294
16
0
15 Dec 2020
Voting based ensemble improves robustness of defensive models
Devvrit
Minhao Cheng
Cho-Jui Hsieh
Inderjit Dhillon
OOD
FedML
AAML
190
13
0
28 Nov 2020
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Empirical Software Engineering (EMSE), 2020
Rui Shu
Tianpei Xia
Laurie A. Williams
Tim Menzies
AAML
231
19
0
23 Nov 2020
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
Hao Fu
A. Veldanda
Prashanth Krishnamurthy
S. Garg
Farshad Khorrami
AAML
306
15
0
04 Nov 2020
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence
Chang Liao
Yao Cheng
Chengfang Fang
Jie Shi
253
1
0
28 Sep 2020
Robust Deep Learning Ensemble against Deception
IEEE Transactions on Dependable and Secure Computing (TDSC), 2020
Wenqi Wei
Ling Liu
AAML
186
29
0
14 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective
ACM Computing Surveys (ACM CSUR), 2020
G. R. Machado
Eugênio Silva
R. Goldschmidt
AAML
306
189
0
08 Sep 2020
TREND: Transferability based Robust ENsemble Design
Deepak Ravikumar
Sangamesh Kodge
Isha Garg
Kaushik Roy
OOD
AAML
200
5
0
04 Aug 2020
Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components
Ken Alparslan
Yigit Can Alparslan
Matthew Burlick
AAML
192
9
0
14 Jul 2020
How benign is benign overfitting?
International Conference on Learning Representations (ICLR), 2020
Amartya Sanyal
P. Dokania
Varun Kanade
Juil Sock
NoLa
AAML
207
61
0
08 Jul 2020
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Ishai Rosenberg
A. Shabtai
Yuval Elovici
Lior Rokach
AAML
319
12
0
05 Jul 2020
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks
Kishor Datta Gupta
Zahid Akhtar
D. Dasgupta
AAML
331
10
0
01 Jul 2020
Biologically Inspired Mechanisms for Adversarial Robustness
M. V. Reddy
Andrzej Banburski
Nishka Pant
T. Poggio
AAML
242
50
0
29 Jun 2020
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks
USENIX Security Symposium (USENIX Security), 2020
Huiying Li
Shawn Shan
Emily Wenger
Jiayun Zhang
Haitao Zheng
Ben Y. Zhao
AAML
335
58
0
24 Jun 2020
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples
Kaleel Mahmood
Deniz Gurevin
Marten van Dijk
Phuong Ha Nguyen
AAML
195
25
0
18 Jun 2020
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
Mahdieh Abbasi
Arezoo Rajabi
Christian Gagné
R. Bobba
AAML
238
17
0
17 May 2020
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition
Tianhang Zheng
Sheng Liu
Changyou Chen
Junsong Yuan
Baochun Li
K. Ren
AAML
297
19
0
14 May 2020
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
Sanchari Sen
Balaraman Ravindran
A. Raghunathan
FedML
AAML
216
69
0
21 Apr 2020
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