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1807.06714
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Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions
18 July 2018
Adnan Siraj Rakin
Jinfeng Yi
Boqing Gong
Deliang Fan
AAML
MQ
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Papers citing
"Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions"
32 / 32 papers shown
Protecting the Neural Networks against FGSM Attack Using Machine Unlearning
Amir Hossein Khorasani
Ali Jahanian
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208
0
0
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MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification
Sajjad Amini
Mohammadreza Teymoorianfard
Shiqing Ma
Amir Houmansadr
OOD
AAML
299
19
0
09 Jun 2024
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise
Zhixun He
Mukesh Singhal
224
1
0
05 Jun 2024
Is ReLU Adversarially Robust?
Korn Sooksatra
Greg Hamerly
Pablo Rivas
173
4
0
06 May 2024
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training
Saurabh Farkya
Aswin Raghavan
Avi Ziskind
257
0
0
30 Nov 2023
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence
IEEE Symposium Series on Computational Intelligence (IEEE-SSCI), 2023
Svetlana Pavlitska
Hannes Grolig
J. Marius Zöllner
AAML
253
5
0
27 Nov 2023
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
Journal of Machine Learning for Modeling and Computing (JMLMC), 2022
Ameya Dilip Jagtap
George Karniadakis
AI4CE
613
93
0
06 Sep 2022
Can collaborative learning be private, robust and scalable?
Dmitrii Usynin
Helena Klause
Johannes C. Paetzold
Daniel Rueckert
Georgios Kaissis
FedML
MedIm
187
3
0
05 May 2022
Robust Binary Models by Pruning Randomly-initialized Networks
Neural Information Processing Systems (NeurIPS), 2022
Chen Liu
Ziqi Zhao
Sabine Süsstrunk
Mathieu Salzmann
TPM
AAML
MQ
199
6
0
03 Feb 2022
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation
AAAI Conference on Artificial Intelligence (AAAI), 2021
Zhongzhi Yu
Y. Fu
Sicheng Li
Chaojian Li
Yingyan Lin
ViT
184
19
0
21 Dec 2021
On the Convergence and Robustness of Adversarial Training
Yisen Wang
Jiabo He
James Bailey
Jinfeng Yi
Bowen Zhou
Quanquan Gu
AAML
577
370
0
15 Dec 2021
Adversarial Attacks Against Deep Generative Models on Data: A Survey
Hui Sun
Tianqing Zhu
Zhiqiu Zhang
Dawei Jin
Wanlei Zhou
AAML
416
64
0
01 Dec 2021
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Neural Information Processing Systems (NeurIPS), 2021
Muhammad Awais
Fengwei Zhou
Chuanlong Xie
Jiawei Li
Sung-Ho Bae
Zhenguo Li
AAML
204
20
0
09 Nov 2021
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness
Chang Song
Riya Ranjan
Xue Yang
MQ
163
4
0
23 Oct 2021
Parameterizing Activation Functions for Adversarial Robustness
Sihui Dai
Saeed Mahloujifar
Prateek Mittal
AAML
183
35
0
11 Oct 2021
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
Yonggan Fu
Yang Zhao
Qixuan Yu
Chaojian Li
Yingyan Lin
AAML
386
15
0
11 Sep 2021
Tensor Normalization and Full Distribution Training
Wolfgang Fuhl
OOD
207
5
0
06 Sep 2021
Improving Adversarial Robustness via Channel-wise Activation Suppressing
International Conference on Learning Representations (ICLR), 2021
Yang Bai
Yuyuan Zeng
Yong Jiang
Shutao Xia
Jiabo He
Yisen Wang
AAML
188
143
0
11 Mar 2021
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Machine-mediated learning (ML), 2020
Jiabo He
Linxi Jiang
Hanxun Huang
Zejia Weng
James Bailey
Yu-Gang Jiang
AAML
281
11
0
24 Jun 2020
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli
Forest Agostinelli
Pierre Baldi
AAML
FAtt
290
43
0
16 Jun 2020
Quantized Neural Networks: Characterization and Holistic Optimization
IEEE Workshop on Signal Processing Systems (SiPS), 2020
Yoonho Boo
Sungho Shin
Wonyong Sung
MQ
164
9
0
31 May 2020
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
Sanchari Sen
Balaraman Ravindran
A. Raghunathan
FedML
AAML
166
68
0
21 Apr 2020
Polarizing Front Ends for Robust CNNs
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020
Can Bakiskan
S. Gopalakrishnan
Metehan Cekic
Upamanyu Madhow
Ramtin Pedarsani
AAML
97
4
0
22 Feb 2020
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), 2019
Yang Song
Qiyu Kang
Wee Peng Tay
AAML
303
23
0
30 Nov 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
International Conference on Cyberworlds (ICC), 2019
Rémi Bernhard
Pierre-Alain Moëllic
J. Dutertre
AAML
MQ
233
18
0
27 Sep 2019
Metric Learning for Adversarial Robustness
Neural Information Processing Systems (NeurIPS), 2019
Chengzhi Mao
Ziyuan Zhong
Junfeng Yang
Carl Vondrick
Baishakhi Ray
OOD
330
201
0
03 Sep 2019
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
Pattern Recognition (Pattern Recognit.), 2019
Jiabo He
Yuhao Niu
Lin Gu
Yisen Wang
Yitian Zhao
James Bailey
Feng Lu
MedIm
AAML
317
516
0
24 Jul 2019
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin
Zhezhi He
Li Yang
Yanzhi Wang
Liqiang Wang
Deliang Fan
AAML
182
21
0
30 May 2019
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin
Chuang Gan
Song Han
MQ
257
211
0
17 Apr 2019
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Faiq Khalid
Hassan Ali
Hammad Tariq
Muhammad Abdullah Hanif
Semeen Rehman
Rehan Ahmed
Mohamed Bennai
AAML
MQ
203
39
0
04 Nov 2018
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
Denise Ratasich
Faiq Khalid
Florian Geissler
Radu Grosu
Mohamed Bennai
E. Bartocci
147
107
0
16 Oct 2018
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only
T. Zheng
Changyou Chen
K. Ren
AAML
110
7
0
10 Oct 2018
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