Evaluation of Parameter-Based Attacks Against Embedded Neural Networks with Laser Injection

Author:

Dumont Mathieu,Hector Kevin,Moëllic Pierre-Alain,Dutertre Jean-Max,Pontié Simon

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (2014)

2. Hou, X., Breier, J., Jap, D., Ma, L., Bhasin, S., Liu, Y.: Security evaluation of deep neural network resistance against laser fault injection. In: Proceedings of the International Symposium on the Physical and Failure Analysis of Integrated Circuits, IPFA, pp. 1–6 (2020)

3. Rakin, A.S., He, Z., Fan, D.: Bit-flip attack: crushing neural network with progressive bit search. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

4. Carlini, N., et al.: On evaluating adversarial robustness. arXiv preprint arXiv:1902.06705 (2019)

5. Tramer, F.: Detecting adversarial examples is (nearly) as hard as classifying them. In: International Conference on Machine Learning, pp. 21692–21702. PMLR (2022)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models;Lecture Notes in Computer Science;2024

2. When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence;Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security;2023-11-26

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