Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914162603458560 |
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| author | Longuet, Delphine Elouazzani, Amira Riveiros, Alejandro Penacho Bastianello, Nicola |
| author_facet | Longuet, Delphine Elouazzani, Amira Riveiros, Alejandro Penacho Bastianello, Nicola |
| contents | Failures in satellite components are costly and challenging to address, often requiring significant human and material resources. Embedding a hybrid AI-based system for fault detection directly in the satellite can greatly reduce this burden by allowing earlier detection. However, such systems must operate with extremely high reliability. To ensure this level of dependability, we employ the formal verification tool Marabou to verify the local robustness of the neural network models used in the AI-based algorithm. This tool allows us to quantify how much a model's input can be perturbed before its output behavior becomes unstable, thereby improving trustworthiness with respect to its performance under uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03948 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case Longuet, Delphine Elouazzani, Amira Riveiros, Alejandro Penacho Bastianello, Nicola Machine Learning Failures in satellite components are costly and challenging to address, often requiring significant human and material resources. Embedding a hybrid AI-based system for fault detection directly in the satellite can greatly reduce this burden by allowing earlier detection. However, such systems must operate with extremely high reliability. To ensure this level of dependability, we employ the formal verification tool Marabou to verify the local robustness of the neural network models used in the AI-based algorithm. This tool allows us to quantify how much a model's input can be perturbed before its output behavior becomes unstable, thereby improving trustworthiness with respect to its performance under uncertainty. |
| title | Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.03948 |