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CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an ExtremeValue Theory (EVT) based robustness score for large-scale deep neural networks(DNNs). In this paper, we propose two extensions on this robustness score.First, we provide a new formal robustness guarantee for classifier functionsthat are twice differentiable. We apply extreme value theory on the new formalrobustness guarantee and the estimated robustness is called second-order CLEVERscore. Second, we discuss how to handle gradient masking, a common defensivetechnique, using CLEVER with Backward Pass Differentiable Approximation (BPDA).With BPDA applied, CLEVER can evaluate the intrinsic robustness of neuralnetworks of a broader class -- networks with non-differentiable inputtransformations. We demonstrate the effectiveness of CLEVER with BPDA inexperiments on a 121-layer Densenet model trained on the ImageNet dataset.

On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm