mlsanityData to 14 Oct 2026

CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness

arXiv 2206.03044 ↗First posted 7 Jun 2022cs.AI cs.LO cs.NEcited by 9

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Abstract (arXiv)

We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and expressive language of the Why3 verification platform. Moreover, CAISAR orchestrates and composes state-of-the-art machine learning verification tools which, individually, are not able to efficiently handle all problems but, collectively, can cover a growing number of properties. Our aim is to assist, on the one hand, the V&V process by reducing the burden of choosing the methodology tailored to a given verification problem, and on the other hand the tools developers by factorizing useful features-visualization, report generation, property description-in one platform. CAISAR will soon be available at https://git.frama-c.com/pub/caisar.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 20 of its 52 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

20 of 52 references.

References · 20

  1. 2016Deep Learning with Differential Privacycited by 2,728
  2. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  3. 2017Formal Verification of Piece-Wise Linear Feed-Forward Neural Networkscited by 242
  4. 2019Algorithms for Verifying Deep Neural Networkscited by 221
  5. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 187
  6. 2018Efficient Formal Safety Analysis of Neural Networkscited by 165
  7. 2019Branch and Bound for Piecewise Linear Neural Network Verificationcited by 138
  8. 2022Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Boundcited by 92
  9. 2020Robustness Verification for Transformerscited by 76
  10. 2018Concolic Testing for Deep Neural Networkscited by 51
  11. 2021Improved Branch and Bound for Neural Network Verification via Lagrangian Decompositioncited by 44
  12. 2020Scaling Polyhedral Neural Network Verification on GPUscited by 43
8 more
  1. 2019Perfectly Parallel Fairness Certification of Neural Networkscited by 42
  2. 2021A Review of Formal Methods applied to Machine Learningcited by 33
  3. 2017Output Range Analysis for Deep Neural Networkscited by 30
  4. 2019Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verificationcited by 28
  5. 2022An Abstraction-Refinement Approach to Verifying Convolutional Neural Networkscited by 24
  6. 2022Efficient Neural Network Analysis with Sum-of-Infeasibilitiescited by 24
  7. 2021DNNV: A Framework for Deep Neural Network Verificationcited by 23
  8. 2019Robustness Verification of Support Vector Machinescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.

  1. 2024Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planescited by 33
  2. 2024Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programscited by 14
  3. 2025Neural Network Verification is a Programming Language Challengecited by 12
  4. 2024Neural Network Verification with PyRATcited by 9
  5. 2025Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verificationcited by 9
  6. 2024Compiling High-Level Neural Network Specifications into VNN-LIB Queriescited by 3
  7. 2025Proof-Carrying Neuro-Symbolic Codecited by 1
  8. 2026VNN-LIB 2.0: Rigorous Foundations for Neural Network Verificationcited by 1
  9. 2025The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verificationcited by 0