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EMPIR: Ensembles of Mixed Precision Deep Networks for Increased
  Robustness against Adversarial Attacks

EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

21 April 2020
Sanchari Sen
Balaraman Ravindran
A. Raghunathan
    FedML
    AAML
ArXivPDFHTML

Papers citing "EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks"

13 / 13 papers shown
Title
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs
Amira Guesmi
B. Ouni
Muhammad Shafique
MQ
AAML
36
0
0
10 Mar 2025
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks
Amira Guesmi
B. Ouni
Muhammad Shafique
AAML
79
0
0
22 Nov 2024
Improved Robustness Against Adaptive Attacks With Ensembles and
  Error-Correcting Output Codes
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes
Thomas Philippon
Christian Gagné
AAML
28
0
0
04 Mar 2023
On the Robustness of Randomized Ensembles to Adversarial Perturbations
On the Robustness of Randomized Ensembles to Adversarial Perturbations
Hassan Dbouk
Naresh R Shanbhag
AAML
23
7
0
02 Feb 2023
Increasing Confidence in Adversarial Robustness Evaluations
Increasing Confidence in Adversarial Robustness Evaluations
Roland S. Zimmermann
Wieland Brendel
Florian Tramèr
Nicholas Carlini
AAML
36
16
0
28 Jun 2022
Building Robust Ensembles via Margin Boosting
Building Robust Ensembles via Margin Boosting
Dinghuai Zhang
Hongyang R. Zhang
Aaron Courville
Yoshua Bengio
Pradeep Ravikumar
A. Suggala
AAML
UQCV
48
15
0
07 Jun 2022
All You Need is RAW: Defending Against Adversarial Attacks with Camera
  Image Pipelines
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines
Yuxuan Zhang
B. Dong
Felix Heide
AAML
26
8
0
16 Dec 2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust
  and Efficient Neural Networks
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Hassan Dbouk
Naresh R Shanbhag
AAML
21
7
0
28 Oct 2021
Improving Adversarial Robustness for Free with Snapshot Ensemble
Improving Adversarial Robustness for Free with Snapshot Ensemble
Yihao Wang
AAML
UQCV
17
1
0
07 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A
  survey
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Ajmal Mian
Navid Kardan
M. Shah
AAML
31
236
0
01 Aug 2021
Tricking Adversarial Attacks To Fail
Tricking Adversarial Attacks To Fail
Blerta Lindqvist
AAML
10
0
0
08 Jun 2020
On Adaptive Attacks to Adversarial Example Defenses
On Adaptive Attacks to Adversarial Example Defenses
Florian Tramèr
Nicholas Carlini
Wieland Brendel
A. Madry
AAML
104
822
0
19 Feb 2020
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
308
5,842
0
08 Jul 2016
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