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Papers
2011.03006
Cited By

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

v1v2 (latest)

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

5 November 2020

Adnan Siraj Rakin

ArXiv (abs)PDF HTML

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

12 / 12 papers shown

Verification of Bit-Flip Attacks against Quantized Neural Networks

Verification of Bit-Flip Attacks against Quantized Neural Networks

280

4

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

447

9

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?

Jacqueline T. Liu

Sabbir Ahmed

Adnan Siraj Rakin

194

6

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 TrainingIEEE International Conference on Computer Vision (ICCV), 2023

164

31

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 ObfuscationInternational Conference on Machine Learning (ICML), 2023

430

30

0

28 Apr 2023

Pentimento: Data Remanence in Cloud FPGAs

Pentimento: Data Remanence in Cloud FPGAsInternational Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2023

David Kohlbrenner

174

6

0

31 Mar 2023

Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks

Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural NetworksUSENIX Security Symposium (USENIX Security), 2023

Qi Li

289

46

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

Kevin A. Roundy

415

115

0

29 Dec 2022

Logic and Reduction Operation based Hardware Trojans in Digital Design

Logic and Reduction Operation based Hardware Trojans in Digital DesignInternational SoC Design Conference (SD), 2022

116

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 EngineeringIEEE Symposium on Field-Programmable Custom Computing Machines (FCCM), 2022

138

11

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-FPGADesign Automation Conference (DAC), 2021

198

47

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 FPGAsInternational Conference on Field-Programmable Technology (ICFPT), 2020

Nicolas Papernot

231

52

0

14 Dec 2020

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