On the adversarial robustness of full integer quantized TinyML models at the edge
Author:
Affiliation:
1. DistriNet, KU Leuven, Leuven, Belgium
Funder
AIDE project funded by the Belgian SPF BOSA
Flemish Research Programme Cybersecurity
Research Fund KU Leuven
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3630180.3631201
Reference27 articles.
1. Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
2. Impact of Low-Bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks
3. Nicholas Carlini and David Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. arXiv:1608.04644 [cs.CR] Nicholas Carlini and David Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. arXiv:1608.04644 [cs.CR]
4. EMNIST: Extending MNIST to handwritten letters
5. Joana C. Costa , Tiago Roxo , Hugo Proença , and Pedro R. M . Inácio . 2023 . How Deep Learning Sees the World : A Survey on Adversarial Attacks & Defenses . arXiv:2305.10862 [cs.CV] Joana C. Costa, Tiago Roxo, Hugo Proença, and Pedro R. M. Inácio. 2023. How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses. arXiv:2305.10862 [cs.CV]
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