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Defend Deep Neural Networks Against Adversarial Examples via Fixed and
  Dynamic Quantized Activation Functions

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
ArXivPDFHTML

Papers citing "Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions"

14 / 14 papers shown
Title
How important are activation functions in regression and classification?
  A survey, performance comparison, and future directions
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
Ameya Dilip Jagtap
George Karniadakis
AI4CE
37
71
0
06 Sep 2022
Robust Binary Models by Pruning Randomly-initialized Networks
Robust Binary Models by Pruning Randomly-initialized Networks
Chen Liu
Ziqi Zhao
Sabine Süsstrunk
Mathieu Salzmann
TPM
AAML
MQ
32
4
0
03 Feb 2022
On the Convergence and Robustness of Adversarial Training
On the Convergence and Robustness of Adversarial Training
Yisen Wang
Xingjun Ma
James Bailey
Jinfeng Yi
Bowen Zhou
Quanquan Gu
AAML
212
345
0
15 Dec 2021
Adversarial Attacks Against Deep Generative Models on Data: A Survey
Adversarial Attacks Against Deep Generative Models on Data: A Survey
Hui Sun
Tianqing Zhu
Zhiqiu Zhang
Dawei Jin
Wanlei Zhou
AAML
42
42
0
01 Dec 2021
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated
  Channel Maps
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Muhammad Awais
Fengwei Zhou
Chuanlong Xie
Jiawei Li
Sung-Ho Bae
Zhenguo Li
AAML
43
17
0
09 Nov 2021
Parameterizing Activation Functions for Adversarial Robustness
Parameterizing Activation Functions for Adversarial Robustness
Sihui Dai
Saeed Mahloujifar
Prateek Mittal
AAML
42
32
0
11 Oct 2021
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
Yonggan Fu
Yang Katie Zhao
Qixuan Yu
Chaojian Li
Yingyan Lin
AAML
52
12
0
11 Sep 2021
SPLASH: Learnable Activation Functions for Improving Accuracy and
  Adversarial Robustness
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli
Forest Agostinelli
Pierre Baldi
AAML
FAtt
36
39
0
16 Jun 2020
Quantized Neural Networks: Characterization and Holistic Optimization
Quantized Neural Networks: Characterization and Holistic Optimization
Yoonho Boo
Sungho Shin
Wonyong Sung
MQ
50
8
0
31 May 2020
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
AAML
MQ
24
18
0
27 Sep 2019
Robust Sparse Regularization: Simultaneously Optimizing Neural Network
  Robustness and Compactness
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin
Zhezhi He
Li Yang
Yanzhi Wang
Liqiang Wang
Deliang Fan
AAML
40
21
0
30 May 2019
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural
  Network against Adversarial Attacks
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
Muhammad Shafique
AAML
MQ
35
37
0
04 Nov 2018
A Roadmap Towards Resilient Internet of Things for Cyber-Physical
  Systems
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
Denise Ratasich
Faiq Khalid
Florian Geissler
Radu Grosu
Muhammad Shafique
E. Bartocci
24
102
0
16 Oct 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
308
5,847
0
08 Jul 2016
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