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On the (Statistical) Detection of Adversarial Examples

On the (Statistical) Detection of Adversarial Examples

21 February 2017
Kathrin Grosse
Praveen Manoharan
Nicolas Papernot
Michael Backes
Patrick McDaniel
    AAML
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Papers citing "On the (Statistical) Detection of Adversarial Examples"

26 / 26 papers shown
Title
Topological Signatures of Adversaries in Multimodal Alignments
Topological Signatures of Adversaries in Multimodal Alignments
Minh Vu
Geigh Zollicoffer
Huy Mai
B. Nebgen
Boian S. Alexandrov
Manish Bhattarai
AAML
84
0
0
29 Jan 2025
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures
Xinheng Xie
Kureha Yamaguchi
Margaux Leblanc
Simon Malzard
Varun Chhabra
Victoria Nockles
Yue-bo Wu
AAML
123
0
0
08 Sep 2024
On Continuity of Robust and Accurate Classifiers
On Continuity of Robust and Accurate Classifiers
Ramin Barati
Reza Safabakhsh
Mohammad Rahmati
AAML
39
1
0
29 Sep 2023
Certifying LLM Safety against Adversarial Prompting
Certifying LLM Safety against Adversarial Prompting
Aounon Kumar
Chirag Agarwal
Suraj Srinivas
Aaron Jiaxun Li
Soheil Feizi
Himabindu Lakkaraju
AAML
47
182
0
06 Sep 2023
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning
Mohamed el Shehaby
Ashraf Matrawy
AAML
55
7
0
08 Jun 2023
Adversarial Examples on Object Recognition: A Comprehensive Survey
Adversarial Examples on Object Recognition: A Comprehensive Survey
A. Serban
E. Poll
Joost Visser
AAML
64
73
0
07 Aug 2020
Anomalous Example Detection in Deep Learning: A Survey
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu
B. Kailkhura
Yue Liu
P. Varshney
D. Song
AAML
98
47
0
16 Mar 2020
Detecting Adversarial Image Examples in Deep Networks with Adaptive
  Noise Reduction
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction
Bin Liang
Hongcheng Li
Miaoqiang Su
Xirong Li
Wenchang Shi
Xiaofeng Wang
AAML
76
216
0
23 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection
  Methods
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini
D. Wagner
AAML
103
1,851
0
20 May 2017
Detecting Adversarial Samples from Artifacts
Detecting Adversarial Samples from Artifacts
Reuben Feinman
Ryan R. Curtin
S. Shintre
Andrew B. Gardner
AAML
74
892
0
01 Mar 2017
On Detecting Adversarial Perturbations
On Detecting Adversarial Perturbations
J. H. Metzen
Tim Genewein
Volker Fischer
Bastian Bischoff
AAML
49
947
0
14 Feb 2017
Adversarial Examples Detection in Deep Networks with Convolutional
  Filter Statistics
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
Xin Li
Fuxin Li
GAN
AAML
87
364
0
22 Dec 2016
Towards Robust Deep Neural Networks with BANG
Towards Robust Deep Neural Networks with BANG
Andras Rozsa
Manuel Günther
Terrance E. Boult
AAML
OOD
36
76
0
01 Dec 2016
Towards the Science of Security and Privacy in Machine Learning
Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot
Patrick McDaniel
Arunesh Sinha
Michael P. Wellman
AAML
58
472
0
11 Nov 2016
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
163
8,497
0
16 Aug 2016
Adversarial Perturbations Against Deep Neural Networks for Malware
  Classification
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Kathrin Grosse
Nicolas Papernot
Praveen Manoharan
Michael Backes
Patrick McDaniel
AAML
38
418
0
14 Jun 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks
  using Adversarial Samples
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
SILM
AAML
81
1,735
0
24 May 2016
Practical Black-Box Attacks against Machine Learning
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
S. Jha
Z. Berkay Celik
A. Swami
MLAU
AAML
44
3,656
0
08 Feb 2016
The Limitations of Deep Learning in Adversarial Settings
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot
Patrick McDaniel
S. Jha
Matt Fredrikson
Z. Berkay Celik
A. Swami
AAML
60
3,947
0
24 Nov 2015
Towards Open Set Deep Networks
Towards Open Set Deep Networks
Abhijit Bendale
Terrance Boult
BDL
EDL
87
1,412
0
19 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep
  Neural Networks
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot
Patrick McDaniel
Xi Wu
S. Jha
A. Swami
AAML
45
3,061
0
14 Nov 2015
Reviewer Integration and Performance Measurement for Malware Detection
Reviewer Integration and Performance Measurement for Malware Detection
Brad Miller
Alex Kantchelian
Michael Carl Tschantz
Sadia Afroz
Rekha Bachwani
...
Vaishaal Shankar
Tony Wu
George Yiu
A. Joseph
J. D. Tygar
35
77
0
26 Oct 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
159
18,922
0
20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
125
3,261
0
05 Dec 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
164
14,831
1
21 Dec 2013
Poisoning Attacks against Support Vector Machines
Poisoning Attacks against Support Vector Machines
Battista Biggio
B. Nelson
Pavel Laskov
AAML
86
1,580
0
27 Jun 2012
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