Certifying Robustness of Convolutional Neural Networks with Tight Linear Approximation. (arXiv:2211.09810v1 [cs.LG])

The robustness of neural network classifiers is becoming important in the
safety-critical domain and can be quantified by robustness verification.
However, at present, efficient and scalable verification techniques are always
sound but incomplete. Therefore, the improvement of certified robustness bounds
is the key criterion to evaluate the superiority of robustness verification
approaches. In this paper, we present a Tight Linear approximation approach for
robustness verification of Convolutional Neural Networks(Ti-Lin). For general
CNNs, we first provide a new linear constraints for …

networks neural networks robustness