Using Z3 for Formal Modeling and Verification of FNN Global Robustness

Using Z3 for Formal Modeling and Verification of FNN Global Robustness

20 April 2023

Zeming Wei

Meng Sun

Papers citing "Using Z3 for Formal Modeling and Verification of FNN Global Robustness"

8 / 8 papers shown

Title
A Survey of Neural Network Robustness Assessment in Image Recognition Jie Wang Jun Ai Minyan Lu Haoran Su Dan Yu Yutao Zhang Junda Zhu Jingyu Liu AAML 35 3 0 12 Apr 2024
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks Ben Batten Mehran Hosseini A. Lomuscio AAML 19 5 0 21 Jan 2024
Enhancing Adversarial Attacks: The Similar Target Method Shuo Zhang Ziruo Wang Zikai Zhou Huanran Chen AAML 57 1 0 21 Aug 2023
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness Zeming Wei Jingyu Zhu Yihao Zhang AAML 32 10 0 09 May 2023
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks Natan Levy Raz Yerushalmi Guy Katz AAML 28 1 0 05 Jan 2023
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages Zeming Wei Xiyue Zhang Meng Sun 88 7 0 27 Jun 2022
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 251 1,842 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 183 933 0 21 Oct 2016