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To compress or not to compress: Understanding the Interactions between
  Adversarial Attacks and Neural Network Compression
v1v2 (latest)

To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression

29 September 2018
Yiren Zhao
Ilia Shumailov
Robert D. Mullins
Ross J. Anderson
    AAML
ArXiv (abs)PDFHTML

Papers citing "To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression"

27 / 27 papers shown
Title
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models
Yoojin Jung
Byung Cheol Song
AAMLVLMMQ
138
0
0
07 Apr 2025
The Inherent Adversarial Robustness of Analog In-Memory Computing
The Inherent Adversarial Robustness of Analog In-Memory Computing
Corey Lammie
Julian Büchel
A. Vasilopoulos
Corey Lammie
Abu Sebastian
AAML
147
4
0
11 Nov 2024
Properties that allow or prohibit transferability of adversarial attacks
  among quantized networks
Properties that allow or prohibit transferability of adversarial attacks among quantized networks
Abhishek Shrestha
Jürgen Grossmann
AAML
68
0
0
15 May 2024
Robustness to distribution shifts of compressed networks for edge
  devices
Robustness to distribution shifts of compressed networks for edge devices
Lulan Shen
Ali Edalati
Brett H. Meyer
Warren Gross
James J. Clark
103
0
0
22 Jan 2024
Bag of Tricks to Boost Adversarial Transferability
Bag of Tricks to Boost Adversarial Transferability
Zeliang Zhang
Rongyi Zhu
Wei Yao
Xiaosen Wang
Chenliang Xu
AAML
129
12
0
16 Jan 2024
Transferable Learned Image Compression-Resistant Adversarial
  Perturbations
Transferable Learned Image Compression-Resistant Adversarial Perturbations
Yang Sui
Zhuohang Li
Ding Ding
Xiang Pan
Xiaozhong Xu
Shan Liu
Zhenzhong Chen
AAML
81
0
0
06 Jan 2024
QVIP: An ILP-based Formal Verification Approach for Quantized Neural
  Networks
QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks
Yedi Zhang
Zhe Zhao
Fu Song
Hao Fei
Tao Chen
Jun Sun
92
20
0
10 Dec 2022
QEBVerif: Quantization Error Bound Verification of Neural Networks
QEBVerif: Quantization Error Bound Verification of Neural Networks
Yedi Zhang
Fu Song
Jun Sun
MQ
141
12
0
06 Dec 2022
Attacking Compressed Vision Transformers
Attacking Compressed Vision Transformers
Swapnil Parekh
Devansh Shah
Pratyush Shukla
AAML
62
1
0
28 Sep 2022
Hardening DNNs against Transfer Attacks during Network Compression using
  Greedy Adversarial Pruning
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning
Jonah O'Brien Weiss
Tiago A. O. Alves
S. Kundu
AAML
61
0
0
15 Jun 2022
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and
  Sparse Network
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
Byung-Kwan Lee
Junho Kim
Y. Ro
AAML
76
23
0
06 Apr 2022
Learning Robust and Lightweight Model through Separable Structured
  Transformations
Learning Robust and Lightweight Model through Separable Structured Transformations
Xian Wei
Yanhui Huang
Yang Xu
Mingsong Chen
Hai Lan
Yuanxiang Li
Zhongfeng Wang
Xuan Tang
OOD
93
0
0
27 Dec 2021
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test
  Accuracy
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Lucas Liebenwein
Cenk Baykal
Brandon Carter
David K Gifford
Daniela Rus
AAML
102
76
0
04 Mar 2021
Adversarially Robust and Explainable Model Compression with On-Device
  Personalization for Text Classification
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification
Yao Qiang
Supriya Tumkur Suresh Kumar
Marco Brocanelli
D. Zhu
AAML
79
0
0
10 Jan 2021
Robustness and Transferability of Universal Attacks on Compressed Models
Robustness and Transferability of Universal Attacks on Compressed Models
Alberto G. Matachana
Kenneth T. Co
Luis Muñoz-González
David Martínez
Emil C. Lupu
AAML
95
11
0
10 Dec 2020
Achieving Adversarial Robustness via Sparsity
Achieving Adversarial Robustness via Sparsity
Shu-Fan Wang
Ningyi Liao
Liyao Xiang
Nanyang Ye
Quanshi Zhang
AAML
99
17
0
11 Sep 2020
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Bai Li
Shiqi Wang
Yunhan Jia
Yantao Lu
Zhenyu Zhong
Lawrence Carin
Suman Jana
AAML
165
14
0
06 Mar 2020
Towards Certifiable Adversarial Sample Detection
Towards Certifiable Adversarial Sample Detection
Ilia Shumailov
Yiren Zhao
Robert D. Mullins
Ross J. Anderson
AAML
65
14
0
20 Feb 2020
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile
  Cloud
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud
Shuang Zhang
Liyao Xiang
Congcong Li
Yixuan Wang
Quanshi Zhang
Zeyu Liu
Yue Liu
FedML
89
1
0
18 Dec 2019
Security of Deep Learning Methodologies: Challenges and Opportunities
Security of Deep Learning Methodologies: Challenges and Opportunities
Shahbaz Rezaei
Xin Liu
AAML
103
4
0
08 Dec 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for
  Embedded Neural Networks
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
Rémi Bernhard
Pierre-Alain Moëllic
J. Dutertre
AAMLMQ
143
18
0
27 Sep 2019
Blackbox Attacks on Reinforcement Learning Agents Using Approximated
  Temporal Information
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
Yiren Zhao
Ilia Shumailov
Han Cui
Xitong Gao
Robert D. Mullins
Ross J. Anderson
AAML
122
31
0
06 Sep 2019
Towards Compact and Robust Deep Neural Networks
Towards Compact and Robust Deep Neural Networks
Vikash Sehwag
Shiqi Wang
Prateek Mittal
Suman Jana
AAML
105
40
0
14 Jun 2019
Adversarially Robust Distillation
Adversarially Robust Distillation
Micah Goldblum
Liam H. Fowl
Soheil Feizi
Tom Goldstein
AAML
135
226
0
23 May 2019
Model Compression with Adversarial Robustness: A Unified Optimization
  Framework
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkNeural Information Processing Systems (NeurIPS), 2025
Shupeng Gui
Haotao Wang
Chen Yu
Haichuan Yang
Zhangyang Wang
Ji Liu
MQ
156
142
0
10 Feb 2019
Sitatapatra: Blocking the Transfer of Adversarial Samples
Sitatapatra: Blocking the Transfer of Adversarial Samples
Ilia Shumailov
Xitong Gao
Yiren Zhao
Robert D. Mullins
Ross J. Anderson
Chengzhong Xu
AAMLGAN
100
15
0
23 Jan 2019
The Taboo Trap: Behavioural Detection of Adversarial Samples
The Taboo Trap: Behavioural Detection of Adversarial Samples
Ilia Shumailov
Yiren Zhao
Robert D. Mullins
Ross J. Anderson
AAML
78
15
0
18 Nov 2018
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