On the Feasibility of Training-time Trojan Attacks through Hardware-based Faults in Memory

Author:

Cai Kunbei1,Zhang Zhenkai2,Yao Fan1

Affiliation:

1. University of Central Florida,Orlando,FL,USA

2. Clemson University,Clemson,SC,USA

Funder

National Science Foundation

Publisher

IEEE

Reference11 articles.

1. Flipping bits in memory without accessing them: An experimental study of dram disturbance errors;kim;IEEE ISCA,0

2. Trojaning Attack on Neural Networks

3. Seeds of SEED: NMT-Stroke: Diverting Neural Machine Translation through Hardware-based Faults

4. ZOO

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2. DeepVenom: Persistent DNN Backdoors Exploiting Transient Weight Perturbations in Memories;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. Spatial Variation-Aware Read Disturbance Defenses: Experimental Analysis of Real DRAM Chips and Implications on Future Solutions;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02

4. An Experimental Analysis of RowHammer in HBM2 DRAM Chips;2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S);2023-06

5. A Survey of Bit-Flip Attacks on Deep Neural Network and Corresponding Defense Methods;Electronics;2023-02-08

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