Compositional Falsification of Cyber-Physical Systems with Machine Learning Components [chapter]

Tommaso Dreossi, Alexandre Donzé, Sanjit A. Seshia
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components lead to
more &raquo; ... failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-57288-8_26">doi:10.1007/978-3-319-57288-8_26</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/evx5o5i5b5hn3iwfaidm6tehvi">fatcat:evx5o5i5b5hn3iwfaidm6tehvi</a> </span>
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