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DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning

DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning

24 November 2019
Samet Demir
Hasan Ferit Eniser
A. Sen
    AAML
ArXivPDFHTML

Papers citing "DeepSmartFuzzer: Reward Guided Test Generation For Deep Learning"

26 / 26 papers shown
Title
Importance-Driven Deep Learning System Testing
Importance-Driven Deep Learning System Testing
Simos Gerasimou
Hasan Ferit Eniser
A. Sen
Alper Çakan
AAML
VLM
47
98
0
09 Feb 2020
Machine Learning Testing: Survey, Landscapes and Horizons
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang
Mark Harman
Lei Ma
Yang Liu
VLM
AILaw
64
744
0
19 Jun 2019
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided
  Fuzzing
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
Xiaofei Xie
Lei Ma
Felix Juefei Xu
Hongxu Chen
Minhui Xue
Yue Liu
Yang Liu
Jianjun Zhao
Jianxiong Yin
Simon See
58
40
0
04 Sep 2018
Guiding Deep Learning System Testing using Surprise Adequacy
Guiding Deep Learning System Testing using Surprise Adequacy
Jinhan Kim
R. Feldt
S. Yoo
AAML
ELM
51
431
0
25 Aug 2018
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena
Ian Goodfellow
AAML
51
321
0
28 Jul 2018
Symbolic Execution for Deep Neural Networks
Symbolic Execution for Deep Neural Networks
D. Gopinath
Kaiyuan Wang
Mengshi Zhang
C. Păsăreanu
S. Khurshid
AAML
49
54
0
27 Jul 2018
Concolic Testing for Deep Neural Networks
Concolic Testing for Deep Neural Networks
Youcheng Sun
Min Wu
Wenjie Ruan
Xiaowei Huang
Marta Kwiatkowska
Daniel Kroening
47
334
0
30 Apr 2018
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma
Felix Juefei Xu
Fuyuan Zhang
Jiyuan Sun
Minhui Xue
...
Ting Su
Li Li
Yang Liu
Jianjun Zhao
Yadong Wang
ELM
62
620
0
20 Mar 2018
Testing Deep Neural Networks
Testing Deep Neural Networks
Youcheng Sun
Xiaowei Huang
Daniel Kroening
James Sharp
Matthew Hill
Rob Ashmore
AAML
49
215
0
10 Mar 2018
Learning to Explain: An Information-Theoretic Perspective on Model
  Interpretation
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen
Le Song
Martin J. Wainwright
Michael I. Jordan
MLT
FAtt
120
568
0
21 Feb 2018
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
Matthew Wicker
Xiaowei Huang
Marta Kwiatkowska
AAML
39
233
0
21 Oct 2017
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous
  Cars
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
Yuchi Tian
Kexin Pei
Suman Jana
Baishakhi Ray
AAML
56
1,353
0
28 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
225
11,962
0
19 Jun 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
Kexin Pei
Yinzhi Cao
Junfeng Yang
Suman Jana
AAML
72
1,357
0
18 May 2017
A Survey on Deep Learning in Medical Image Analysis
A Survey on Deep Learning in Medical Image Analysis
G. Litjens
Thijs Kooi
B. Bejnordi
A. Setio
F. Ciompi
Mohsen Ghafoorian
Jeroen van der Laak
Bram van Ginneken
C. I. Sánchez
OOD
553
10,726
0
19 Feb 2017
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
165
8,513
0
16 Aug 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
489
5,878
0
08 Jul 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
End to End Learning for Self-Driving Cars
End to End Learning for Self-Driving Cars
Mariusz Bojarski
D. Testa
Daniel Dworakowski
Bernhard Firner
B. Flepp
...
Urs Muller
Jiakai Zhang
Xin Zhang
Jake Zhao
Karol Zieba
SSL
46
4,153
0
25 Apr 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
47
3,660
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
66
3,947
0
24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
93
4,878
0
14 Nov 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
161
18,922
0
20 Dec 2014
Sequence to Sequence Learning with Neural Networks
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever
Oriol Vinyals
Quoc V. Le
AIMat
275
20,491
0
10 Sep 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
166
14,831
1
21 Dec 2013
Multi-column Deep Neural Networks for Image Classification
Multi-column Deep Neural Networks for Image Classification
D. Ciresan
U. Meier
Jürgen Schmidhuber
106
3,935
0
13 Feb 2012
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