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ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based
  Repeated Bit Flip Attack
v1v2 (latest)

ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack

1 November 2021
Dahoon Park
K. Kwon
Sunghoon Im
Jaeha Kung
    AAML
ArXiv (abs)PDFHTML

Papers citing "ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack"

30 / 30 papers shown
Title
Retrospective: Flipping Bits in Memory Without Accessing Them: An
  Experimental Study of DRAM Disturbance Errors
Retrospective: Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors
O. Mutlu
76
576
0
28 Jun 2023
Very Deep Transformers for Neural Machine Translation
Very Deep Transformers for Neural Machine Translation
Xiaodong Liu
Kevin Duh
Liyuan Liu
Jianfeng Gao
60
104
0
18 Aug 2020
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Adnan Siraj Rakin
Zhezhi He
Jingtao Li
Fan Yao
C. Chakrabarti
Deliang Fan
AAML
50
13
0
24 Jul 2020
Language Models are Few-Shot Learners
Language Models are Few-Shot Learners
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
...
Christopher Berner
Sam McCandlish
Alec Radford
Ilya Sutskever
Dario Amodei
BDL
865
42,379
0
28 May 2020
ZeroQ: A Novel Zero Shot Quantization Framework
ZeroQ: A Novel Zero Shot Quantization Framework
Yaohui Cai
Z. Yao
Zhen Dong
A. Gholami
Michael W. Mahoney
Kurt Keutzer
MQ
98
399
0
01 Jan 2020
A Survey of Deep Learning Techniques for Autonomous Driving
A Survey of Deep Learning Techniques for Autonomous Driving
Sorin Grigorescu
Bogdan Trasnea
Tiberiu T. Cocias
G. Macesanu
3DPC
86
1,401
0
17 Oct 2019
High Accuracy and High Fidelity Extraction of Neural Networks
High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski
Nicholas Carlini
David Berthelot
Alexey Kurakin
Nicolas Papernot
MLAUMIACV
81
380
0
03 Sep 2019
Fooling a Real Car with Adversarial Traffic Signs
Fooling a Real Car with Adversarial Traffic Signs
N. Morgulis
Alexander Kreines
Shachar Mendelowitz
Yuval Weisglass
AAML
63
92
0
30 Jun 2019
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural
  Networks
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Pu Zhao
Siyue Wang
Cheng Gongye
Yanzhi Wang
Yunsi Fei
Xinyu Lin
AAML
46
76
0
28 May 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan
Quoc V. Le
3DVMedIm
151
18,179
0
28 May 2019
RowHammer: A Retrospective
RowHammer: A Retrospective
O. Mutlu
Jeremie S. Kim
70
231
0
22 Apr 2019
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4
  Hours
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Dimitrios Stamoulis
Ruizhou Ding
Di Wang
Dimitrios Lymberopoulos
B. Priyantha
Jie Liu
Diana Marculescu
60
285
0
05 Apr 2019
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Adnan Siraj Rakin
Zhezhi He
Deliang Fan
AAML
69
224
0
28 Mar 2019
Learning Gentle Object Manipulation with Curiosity-Driven Deep
  Reinforcement Learning
Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning
Sandy H. Huang
Martina Zambelli
Jackie Kay
M. Martins
Yuval Tassa
P. Pilarski
R. Hadsell
64
51
0
20 Mar 2019
MnasNet: Platform-Aware Neural Architecture Search for Mobile
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan
Bo Chen
Ruoming Pang
Vijay Vasudevan
Mark Sandler
Andrew G. Howard
Quoc V. Le
MQ
126
3,015
0
31 Jul 2018
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
  Design
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma
Xiangyu Zhang
Haitao Zheng
Jian Sun
181
5,006
0
30 Jul 2018
PRADA: Protecting against DNN Model Stealing Attacks
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti
S. Szyller
Samuel Marchal
Nadarajah Asokan
SILMAAML
70
443
0
07 May 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler
Andrew G. Howard
Menglong Zhu
A. Zhmoginov
Liang-Chieh Chen
204
19,333
0
13 Jan 2018
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the
  iCub Humanoid
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Marco Melis
Ambra Demontis
Battista Biggio
Gavin Brown
Giorgio Fumera
Fabio Roli
AAML
52
98
0
23 Aug 2017
Adversarial Transformation Networks: Learning to Generate Adversarial
  Examples
Adversarial Transformation Networks: Learning to Generate Adversarial Examples
S. Baluja
Ian S. Fischer
GAN
79
286
0
28 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
324
20,086
0
07 Oct 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILMAAML
545
5,910
0
08 Jul 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,426
0
10 Dec 2015
Rethinking the Inception Architecture for Computer Vision
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy
Vincent Vanhoucke
Sergey Ioffe
Jonathon Shlens
Z. Wojna
3DVBDL
886
27,416
0
02 Dec 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
154
4,905
0
14 Nov 2015
You Only Look Once: Unified, Real-Time Object Detection
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon
S. Divvala
Ross B. Girshick
Ali Farhadi
ObjD
718
37,020
0
08 Jun 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAMLGAN
282
19,121
0
20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,508
0
04 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
282
14,963
1
21 Dec 2013
Playing Atari with Deep Reinforcement Learning
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih
Koray Kavukcuoglu
David Silver
Alex Graves
Ioannis Antonoglou
Daan Wierstra
Martin Riedmiller
129
12,265
0
19 Dec 2013
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