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1902.03151
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Discretization based Solutions for Secure Machine Learning against Adversarial Attacks
IEEE Access (IEEE Access), 2019
8 February 2019
Priyadarshini Panda
I. Chakraborty
Kaushik Roy
AAML
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Papers citing
"Discretization based Solutions for Secure Machine Learning against Adversarial Attacks"
26 / 26 papers shown
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification
Reek Majumder
M. Chowdhury
S. Khan
Zadid Khan
Fahim Ahmad
Frank Ngeni
G. Comert
Judith Mwakalonge
Dimitra Michalaka
AAML
171
2
0
17 Apr 2025
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers
Seyed Parsa Neshaei
Yasaman Boreshban
Gholamreza Ghassem-Sani
Seyed Abolghasem Mirroshandel
MQ
242
2
0
08 Mar 2024
Discretization-based ensemble model for robust learning in IoT
International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous), 2023
Anahita Namvar
Chandra Thapa
S. Kanhere
AAML
OOD
239
4
0
18 Jul 2023
Approximate Computing and the Efficient Machine Learning Expedition
J. Henkel
Hai Helen Li
A. Raghunathan
M. Tahoori
Swagath Venkataramani
Xiaoxuan Yang
Georgios Zervakis
274
24
0
02 Oct 2022
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
ACM Computing Surveys (ACM CSUR), 2022
Giorgos Armeniakos
Georgios Zervakis
Dimitrios Soudris
J. Henkel
589
136
0
16 Mar 2022
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
494
16
0
11 Sep 2021
Tensor Normalization and Full Distribution Training
Wolfgang Fuhl
OOD
352
5
0
06 Sep 2021
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks
Design, Automation and Test in Europe (DATE), 2021
Abhiroop Bhattacharjee
Abhishek Moitra
Priyadarshini Panda
AAML
223
8
0
09 May 2021
Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective
Journal of Information Security and Applications (JISA), 2021
Pulei Xiong
Scott Buffett
Shahrear Iqbal
Philippe Lamontagne
M. Mamun
Heather Molyneaux
OOD
413
19
0
08 Jan 2021
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD), 2021
Rachel Sterneck
Abhishek Moitra
Priyadarshini Panda
AAML
173
9
0
05 Jan 2021
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks
Abhishek Moitra
Priyadarshini Panda
AAML
287
2
0
26 Nov 2020
Blockchain based Attack Detection on Machine Learning Algorithms for IoT based E-Health Applications
Thippa Reddy Gadekallu
Manoj M K
Sivarama Krishnan S
Neeraj Kumar
S. Hakak
S. Bhattacharya
OOD
231
63
0
03 Nov 2020
Defending against substitute model black box adversarial attacks with the 01 loss
Yunzhe Xue
Meiyan Xie
Usman Roshan
AAML
141
1
0
01 Sep 2020
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks
Abhiroop Bhattacharjee
Priyadarshini Panda
AAML
245
19
0
25 Aug 2020
Towards adversarial robustness with 01 loss neural networks
Yunzhe Xue
Meiyan Xie
Usman Roshan
OOD
AAML
237
5
0
20 Aug 2020
TREND: Transferability based Robust ENsemble Design
Deepak Ravikumar
Sangamesh Kodge
Isha Garg
Kaushik Roy
OOD
AAML
206
5
0
04 Aug 2020
Towards Understanding the Effect of Leak in Spiking Neural Networks
Sayeed Shafayet Chowdhury
Chankyu Lee
Kaushik Roy
247
70
0
15 Jun 2020
On the transferability of adversarial examples between convex and 01 loss models
International Conference on Machine Learning and Applications (ICMLA), 2020
Yunzhe Xue
Meiyan Xie
Usman Roshan
AAML
191
7
0
14 Jun 2020
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
H. Hashemi
Yongqin Wang
M. Annavaram
FedML
309
27
0
01 Jun 2020
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks
Priyadarshini Panda
MQ
AAML
261
30
0
22 Apr 2020
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
Sanchari Sen
Balaraman Ravindran
A. Raghunathan
FedML
AAML
237
69
0
21 Apr 2020
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations
European Conference on Computer Vision (ECCV), 2020
Saima Sharmin
Nitin Rathi
Priyadarshini Panda
Kaushik Roy
AAML
417
111
0
23 Mar 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
172
4
0
22 Feb 2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
International Conference on Machine Learning (ICML), 2020
Florian Tramèr
Jens Behrmann
Nicholas Carlini
Nicolas Papernot
J. Jacobsen
AAML
SILM
268
102
0
11 Feb 2020
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020
Ling Liang
Xing Hu
Lei Deng
Yujie Wu
Guoqi Li
Yufei Ding
Peng Li
Yuan Xie
AAML
358
81
0
01 Jan 2020
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
Xiaodong Wu
Hui Xie
Leixin Zhou
Xiaodong Wu
Weiyu Xu
R. Mudumbai
AAML
249
7
0
25 May 2019
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