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Manipulating SGD with Data Ordering Attacks

Manipulating SGD with Data Ordering Attacks

19 April 2021
Ilia Shumailov
Zakhar Shumaylov
Dmitry Kazhdan
Yiren Zhao
Nicolas Papernot
Murat A. Erdogdu
Ross J. Anderson
    AAML
ArXivPDFHTML

Papers citing "Manipulating SGD with Data Ordering Attacks"

25 / 25 papers shown
Title
SEPS: A Separability Measure for Robust Unlearning in LLMs
SEPS: A Separability Measure for Robust Unlearning in LLMs
Wonje Jeung
Sangyeon Yoon
Albert No
MU
VLM
151
0
0
20 May 2025
PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
Xuzhao Li
Jingyi Wang
Xiaohan Yuan
Peixin Zhang
Zhan Qin
Peng Kuang
Kui Ren
AAML
MU
77
0
0
10 May 2025
Position: A taxonomy for reporting and describing AI security incidents
Position: A taxonomy for reporting and describing AI security incidents
L. Bieringer
Kevin Paeth
Andreas Wespi
Kathrin Grosse
Alexandre Alahi
Kathrin Grosse
125
0
0
19 Dec 2024
Verifiable and Provably Secure Machine Unlearning
Verifiable and Provably Secure Machine Unlearning
Thorsten Eisenhofer
Doreen Riepel
Varun Chandrasekaran
Esha Ghosh
O. Ohrimenko
Nicolas Papernot
AAML
MU
65
26
0
17 Oct 2022
On the Origin of Implicit Regularization in Stochastic Gradient Descent
On the Origin of Implicit Regularization in Stochastic Gradient Descent
Samuel L. Smith
Benoit Dherin
David Barrett
Soham De
MLT
27
202
0
28 Jan 2021
Poisoned classifiers are not only backdoored, they are fundamentally
  broken
Poisoned classifiers are not only backdoored, they are fundamentally broken
Mingjie Sun
Siddhant Agarwal
J. Zico Kolter
26
26
0
18 Oct 2020
An Analysis of Constant Step Size SGD in the Non-convex Regime:
  Asymptotic Normality and Bias
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Lu Yu
Krishnakumar Balasubramanian
S. Volgushev
Murat A. Erdogdu
77
50
0
14 Jun 2020
Sponge Examples: Energy-Latency Attacks on Neural Networks
Sponge Examples: Energy-Latency Attacks on Neural Networks
Ilia Shumailov
Yiren Zhao
Daniel Bates
Nicolas Papernot
Robert D. Mullins
Ross J. Anderson
SILM
46
129
0
05 Jun 2020
Dynamic Backdoor Attacks Against Machine Learning Models
Dynamic Backdoor Attacks Against Machine Learning Models
A. Salem
Rui Wen
Michael Backes
Shiqing Ma
Yang Zhang
AAML
99
275
0
07 Mar 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
501
4,308
0
23 Aug 2019
On The Power of Curriculum Learning in Training Deep Networks
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen
D. Weinshall
ODL
68
443
0
07 Apr 2019
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi
Wenjie Huang
Mahyar Najibi
Octavian Suciu
Christoph Studer
Tudor Dumitras
Tom Goldstein
AAML
84
1,080
0
03 Apr 2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for
  Regression Learning
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski
Alina Oprea
Battista Biggio
Chang-rui Liu
Cristina Nita-Rotaru
Yue Liu
AAML
85
757
0
01 Apr 2018
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Xinyun Chen
Chang-rui Liu
Yue Liu
Kimberly Lu
D. Song
AAML
SILM
86
1,822
0
15 Dec 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model
  Supply Chain
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu
Brendan Dolan-Gavitt
S. Garg
SILM
85
1,758
0
22 Aug 2017
Evasion Attacks against Machine Learning at Test Time
Evasion Attacks against Machine Learning at Test Time
Battista Biggio
Igino Corona
Davide Maiorca
B. Nelson
Nedim Srndic
Pavel Laskov
Giorgio Giacinto
Fabio Roli
AAML
106
2,142
0
21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
241
11,962
0
19 Jun 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
1.1K
20,747
0
17 Apr 2017
Practical Coreset Constructions for Machine Learning
Practical Coreset Constructions for Machine Learning
Olivier Bachem
Mario Lucic
Andreas Krause
57
185
0
19 Mar 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a
  nonasymptotic analysis
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Maxim Raginsky
Alexander Rakhlin
Matus Telgarsky
67
518
0
13 Feb 2017
Practical Black-Box Attacks against Machine Learning
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
S. Jha
Z. Berkay Celik
A. Swami
MLAU
AAML
57
3,660
0
08 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.6K
192,638
0
10 Dec 2015
Character-level Convolutional Networks for Text Classification
Character-level Convolutional Networks for Text Classification
Xiang Zhang
Jiaqi Zhao
Yann LeCun
215
6,077
0
04 Sep 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
201
18,922
0
20 Dec 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
204
14,831
1
21 Dec 2013
1