\textsf{CLEVEREST}: accelerating CEGAR-based neural network verification via adversarial attacks
From MaRDI portal
Publication:6109431
DOI10.1007/978-3-031-22308-2_20zbMath1524.68328MaRDI QIDQ6109431
Zhe Zhao, Guangke Chen, Fu Song, Taolue Chen, Yedi Zhang, Jiaxiang Liu
Publication date: 28 July 2023
Published in: Static Analysis (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Specification and verification (program logics, model checking, etc.) (68Q60)
Cites Work
- Unnamed Item
- Unnamed Item
- \textsf{BDD4BNN}: a BDD-based quantitative analysis framework for binarized neural networks
- Improving neural network verification through spurious region guided refinement
- Enhancing robustness verification for deep neural networks via symbolic propagation
- Static analysis of ReLU neural networks with tropical polyhedra
- Verifying low-dimensional input neural networks via input quantization
- Safety verification of deep neural networks
- Reluplex: an efficient SMT solver for verifying deep neural networks
- An abstraction-based framework for neural network verification
- Abstract neural networks
- Probabilistic Lipschitz analysis of neural networks
- Counterexample-guided abstraction refinement for symbolic model checking
- Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
This page was built for publication: \textsf{CLEVEREST}: accelerating CEGAR-based neural network verification via adversarial attacks