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PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach

Tsui-Wei Weng · Pin-Yu Chen · Lam Nguyen · Mark Squillante · Akhilan Boopathy · Ivan Oseledets · Luca Daniel

Pacific Ballroom #70

Keywords: [ Algorithms ] [ Adversarial Examples ]

Abstract: We propose a novel framework PROVEN to \textbf{PRO}babilistically \textbf{VE}rify \textbf{N}eural network's robustness with statistical guarantees. PROVEN provides probability certificates of neural network robustness when the input perturbation follow distributional characterization. Notably, PROVEN is derived from current state-of-the-art worst-case neural network robustness verification frameworks, and therefore it can provide probability certificates with little computational overhead on top of existing methods such as Fast-Lin, CROWN and CNN-Cert. Experiments on small and large MNIST and CIFAR neural network models demonstrate our probabilistic approach can tighten up robustness certificate to around $1.8 \times$ and $3.5 \times$ with at least a $99.99\%$ confidence compared with the worst-case robustness certificate by CROWN and CNN-Cert.

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