In this short paper, we present our ongoing work on the veriFIRE project—a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system. The system we target is an airborne platform for wildfire detection, which incorporates two deep neural networks. We describe the system and its properties of interest, and discuss our attempts to verify the system’s consistency, i.e., its ability to continue and correctly classify a given input, even if the wildfire it describes increases in intensity. We regard this work as a step towards the incorporation of academic-oriented verification tools into real-world systems of interest.
|Original language||American English|
|Title of host publication||Formal Methods - 25th International Symposium, FM 2023, Proceedings|
|Editors||Marsha Chechik, Joost-Pieter Katoen, Martin Leucker|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||9|
|State||Published - 2023|
|Event||25th International Symposium on Formal Methods, FM 2023 - Lübeck, Germany|
Duration: 6 Mar 2023 → 10 Mar 2023
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||25th International Symposium on Formal Methods, FM 2023|
|Period||6/03/23 → 10/03/23|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by a grant from the Israel Innovation Authority. The work of Amir was also supported by a scholarship from the Clore Israel Foundation.
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.