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Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
[article]
2019
arXiv
pre-print
In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small perturbations to an input do not cause the network to perform misclassifications. In this paper, we present a novel algorithm for verifying robustness properties of neural networks. Our method synergistically combines gradient-based optimization methods for counterexample search with abstraction-based proof search to
arXiv:1904.09959v1
fatcat:7khytmrwprfvlppxugkrf3drae