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DeepSafe: a data-driven approach for assessing robustness of neural networks

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Publication:6109575
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DOI10.1007/978-3-030-01090-4_1zbMath1517.68342MaRDI QIDQ6109575

Guy Katz, Divya Gopinath, Corina S. Păsăreanu, Clark Barrett

Publication date: 28 July 2023

Published in: Automated Technology for Verification and Analysis (Search for Journal in Brave)



Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Automated systems (robots, etc.) in control theory (93C85)


Related Items (6)

DiffRNN: differential verification of recurrent neural networks ⋮ Robustness verification of semantic segmentation neural networks using relaxed reachability ⋮ Metrics and methods for robustness evaluation of neural networks with generative models ⋮ Reluplex: a calculus for reasoning about deep neural networks ⋮ An SMT-based approach for verifying binarized neural networks ⋮ A game-based approximate verification of deep neural networks with provable guarantees




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