Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
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One can formalise system-level safety for autonomous vehicles even when it is not possible to formalise correctness for object detection and classification components in the autonomy stack.
“one can formalise system-level safety for autonomous vehicles even when it is not possible to formalise correctness for object detection and classification components in the autonomy stack”
From Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems (2024) · cites this paper as Dreossi et al., 2019
A decomposition of the system into components (spatial and/or temporal) has been shown to aid in scalable analysis.
“Such a decomposition could be spatial and/or temporal and has been shown to aid in scalable analysis (Dreossi et al., 2019;Yalcinkaya et al., 2023).”
From Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems (2024) · cites this paper as Dreossi et al., 2019
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Towards Evaluating the Robustness of Neural Networks
Carlini, N., & Wagner, D. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.49 · Cited by 8,859 · see citing works
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A Review on Human–Machine Trust Evaluation: Human-Centric and Machine-Centric Perspectives
Gebru, B., Zeleke, L., Blankson, D., Nabil, M., Nateghi, S., Homaifar, A., & Tunstel, E. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3144956 · Cited by 101 · see citing works
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
Dalrymple, D. "., Skalse, J., Bengio, Y., Russell, S., Tegmark, M., Seshia, S., Omohundro, S., Szegedy, C., Goldhaber, B., Ammann, N., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.06624 · Cited by 12 · see citing works
Self-citation
- Authors
- Dreossi, T., Donzé, A., & Seshia, S. A.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Dreossi, Tommaso
- Venue
- Journal of Automated Reasoning
- Published
- 2019-01-19
- Volume
- 63
- Issue
- 4
- Pages
- 1031-1053
- ISSN
- 0168-7433 (ISSN-L)
- 1573-0670
- OpenAlex
- W2604347212
- Cited by
- 124
- References
- 4
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 15 · 2023: 21 · 2022: 23 · 2021: 22 · 2020: 17 · 2019: 7 · 2018: 1
- FWCI
- 11.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Automated Reasoning (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency (FA8750-16-C-0043)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- cyber-physical systems, falsification, signal temporal logic, adversarial perturbations, deep neural network, automatic emergency braking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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