Transfer Adversarial Attacks through Approximate Computing
Author:
Affiliation:
1. University of Naples Federico II, Italy
2. Università degli Studi di Napoli Federico II, Italy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3664476.3670449
Reference34 articles.
1. Special Session: Approximation and Fault Resiliency of DNN Accelerators
2. Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers
3. Low-epsilon adversarial attack against a neural network online image stream classifier
4. Mohammad Javad Askarizadeh Ebrahim Farahmand Jorge Castro-Godinez Ali Mahani Laura Cabrera-Quiros and Carlos Salazar-Garcia. 2024. Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers. http://arxiv.org/abs/2404.11665 arXiv:2404.11665 [cs].
5. Investigating the Resilience Source of Classification Systems for Approximate Computing Techniques
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