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Found 5 papers, 0 papers with code
Verification of Neural Networks' Global Robustness
We evaluate VHAGaR on several datasets and classifiers and show that, given a three hour timeout, the average gap between the lower and upper bound on the minimal globally robust bound computed by VHAGaR is 1. 9, while the gap of an existing global robustness verifier is 154. 7.
Verification of Neural Networks Local Differential Classification Privacy
We propose Sphynx, an algorithm that computes an abstraction of all networks, with a high probability, from a small set of networks, and verifies LDCP directly on the abstract network.
Boosting Robustness Verification of Semantic Feature Neighborhoods
Deep neural networks have been shown to be vulnerable to adversarial attacks that perturb inputs based on semantic features.
Towards Scaling Robustness Verification of Semantic Features via Proof Velocity
We focus on robustness to perturbations of semantic features and introduce the concept of proof guided by velocity to scale the analysis.
NETWORK ROBUSTNESS TO PCA PERTURBATIONS
In this work, we take a new approach and study the robustness of networks to the inputs' semantic features.
