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Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush
  Deep Neural Network in Multi-Tenant FPGA

Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA

5 November 2020
Adnan Siraj Rakin
Yukui Luo
Xiaolin Xu
Deliang Fan
    AAML
ArXivPDFHTML

Papers citing "Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA"

12 / 12 papers shown
Title
Verification of Bit-Flip Attacks against Quantized Neural Networks
Verification of Bit-Flip Attacks against Quantized Neural Networks
Yedi Zhang
Lei Huang
Pengfei Gao
Fu Song
Jun Sun
Jin Song Dong
AAML
49
0
0
22 Feb 2025
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning
Dayong Ye
Tainqing Zhu
J. Li
Kun Gao
B. Liu
L. Zhang
Wanlei Zhou
Y. Zhang
AAML
MU
80
0
0
28 Jan 2025
Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms
  Truly Safeguard DRAM?
Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM?
Ranyang Zhou
Jacqueline T. Liu
Sabbir Ahmed
Nakul Kochar
Adnan Siraj Rakin
Shaahin Angizi
16
5
0
28 Nov 2023
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
Jianshuo Dong
Han Qiu
Yiming Li
Tianwei Zhang
Yuan-Fang Li
Zeqi Lai
Chao Zhang
Shutao Xia
AAML
28
13
0
12 Aug 2023
NNSplitter: An Active Defense Solution for DNN Model via Automated
  Weight Obfuscation
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation
Tong Zhou
Yukui Luo
Shaolei Ren
Xiaolin Xu
AAML
49
15
0
28 Apr 2023
Pentimento: Data Remanence in Cloud FPGAs
Pentimento: Data Remanence in Cloud FPGAs
Colin Drewes
Olivia Weng
Andres Meza
Alric Althoff
David Kohlbrenner
Ryan Kastner
D. Richmond
18
4
0
31 Mar 2023
Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks
Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks
Jialai Wang
Ziyuan Zhang
Meiqi Wang
Han Qiu
Tianwei Zhang
Qi Li
Zongpeng Li
Tao Wei
Chao Zhang
AAML
22
20
0
27 Feb 2023
"Real Attackers Don't Compute Gradients": Bridging the Gap Between
  Adversarial ML Research and Practice
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese
Hyrum S. Anderson
Savino Dambra
D. Freeman
Fabio Pierazzi
Kevin A. Roundy
AAML
31
75
0
29 Dec 2022
Logic and Reduction Operation based Hardware Trojans in Digital Design
Logic and Reduction Operation based Hardware Trojans in Digital Design
Mayukhmali Das
Sounak Dutta
S. Chatterjee
6
0
0
09 Sep 2022
NNReArch: A Tensor Program Scheduling Framework Against Neural Network
  Architecture Reverse Engineering
NNReArch: A Tensor Program Scheduling Framework Against Neural Network Architecture Reverse Engineering
Yukui Luo
Shijin Duan
Gongye Cheng
Yunsi Fei
Xiaolin Xu
9
8
0
22 Mar 2022
DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator
  in Cloud-FPGA
DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA
Yukui Luo
Cheng Gongye
Yunsi Fei
Xiaolin Xu
6
34
0
20 May 2021
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators
  on Multi-Tenant FPGAs
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs
Andrew Boutros
Mathew Hall
Nicolas Papernot
Vaughn Betz
11
38
0
14 Dec 2020
1