mlsanityData to 7 Oct 2026

veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System

arXiv 2212.03287 ↗First posted 6 Dec 2022cs.LO cs.LG cs.SEcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

28 of 50 references.

References · 28

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16 more
  1. 2019An Abstraction-Based Framework for Neural Network Verificationcited by 68
  2. 2019Quantitative Verification of Neural Networks And its Security Applicationscited by 56
  3. 2020An SMT-Based Approach for Verifying Binarized Neural Networkscited by 47
  4. 2019Fastened CROWN: Tightened Neural Network Robustness Certificatescited by 39
  5. 2021Neural Network Robustness as a Verification Property: A Principled Case Studycited by 35
  6. 2021Towards Scalable Verification of Deep Reinforcement Learningcited by 33
  7. 2022Verifying Learning-Based Robotic Navigation Systemscited by 33
  8. 2020Global Optimization of Objective Functions Represented by ReLU Networkscited by 24
  9. 2022An Abstraction-Refinement Approach to Verifying Convolutional Neural Networkscited by 24
  10. 2022Neural Network Verification with Proof Productioncited by 22
  11. 2022Verification-Aided Deep Ensemble Selectioncited by 18
  12. 2022Neural Network Verification using Residual Reasoningcited by 18
  13. 2021Minimal Multi-Layer Modifications of Deep Neural Networkscited by 14
  14. 2018Toward Scalable Verification for Safety-Critical Deep Networkscited by 12
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 2; 2026: 1.

12 papers.

  1. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
  2. 2024Local vs. Global Interpretability: A Computational Complexity Perspectivecited by 23
  3. 2024Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificatescited by 15
  4. 2024Verification-Guided Shielding for Deep Reinforcement Learningcited by 13
  5. 2024Shield Synthesis for LTL Modulo Theoriescited by 12
  6. 2023Verifying Generalization in Deep Learningcited by 11
  7. 2024Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretationcited by 11
  8. 2024Analyzing Adversarial Inputs in Deep Reinforcement Learningcited by 7
  9. 2025Proof Minimization in Neural Network Verificationcited by 4
  10. 2023DelBugV: Delta-Debugging Neural Network Verifierscited by 3
  11. 2025PICID: Proof-Driven Clause Learning in Neural Network Verificationcited by 3
  12. 2026veriFIRE: an Industrial Case Study in Verifying Consistency Properties for a DNN-Based Wildfire Detection Systemcited by 0