Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913419064508416 |
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| author | Banitaba, Faeze S. Aygun, Sercan Najafi, M. Hassan |
| author_facet | Banitaba, Faeze S. Aygun, Sercan Najafi, M. Hassan |
| contents | In neural network (NN) security, safeguarding model integrity and resilience against adversarial attacks has become paramount. This study investigates the application of stochastic computing (SC) as a novel mechanism to fortify NN models. The primary objective is to assess the efficacy of SC to mitigate the deleterious impact of attacks on NN results. Through a series of rigorous experiments and evaluations, we explore the resilience of NNs employing SC when subjected to adversarial attacks. Our findings reveal that SC introduces a robust layer of defense, significantly reducing the susceptibility of networks to attack-induced alterations in their outcomes. This research contributes novel insights into the development of more secure and reliable NN systems, essential for applications in sensitive domains where data integrity is of utmost concern. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing Banitaba, Faeze S. Aygun, Sercan Najafi, M. Hassan Cryptography and Security Emerging Technologies In neural network (NN) security, safeguarding model integrity and resilience against adversarial attacks has become paramount. This study investigates the application of stochastic computing (SC) as a novel mechanism to fortify NN models. The primary objective is to assess the efficacy of SC to mitigate the deleterious impact of attacks on NN results. Through a series of rigorous experiments and evaluations, we explore the resilience of NNs employing SC when subjected to adversarial attacks. Our findings reveal that SC introduces a robust layer of defense, significantly reducing the susceptibility of networks to attack-induced alterations in their outcomes. This research contributes novel insights into the development of more secure and reliable NN systems, essential for applications in sensitive domains where data integrity is of utmost concern. |
| title | Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing |
| topic | Cryptography and Security Emerging Technologies |
| url | https://arxiv.org/abs/2407.04861 |