On the Certified Robustness for Ensemble Models and Beyond. (arXiv:2107.10873v1 [cs.LG])

Recent studies show that deep neural networks (DNN) are vulnerable to
adversarial examples, which aim to mislead DNNs by adding perturbations with
small magnitude. To defend against such attacks, both empirical and theoretical
defense approaches have been extensively studied for a single ML model. In this
work, we aim to analyze and provide the certified robustness for ensemble ML
models, together with the sufficient and necessary conditions of robustness for
different ensemble protocols. Although ensemble models are shown more robust
than a single model empirically; surprisingly, we find that in terms of the
certified robustness the standard ensemble models only achieve marginal
improvement compared to a single model. Thus, to explore the conditions that
guarantee to provide certifiably robust ensemble ML models, we first prove that
diversified gradient and large confidence margin are sufficient and necessary
conditions for certifiably robust ensemble models under the model-smoothness
assumption. We then provide the bounded model-smoothness analysis based on the
proposed Ensemble-before-Smoothing strategy. We also prove that an ensemble
model can always achieve higher certified robustness than a single base model
under mild conditions. Inspired by the theoretical findings, we propose the
lightweight Diversity Regularized Training (DRT) to train certifiably robust
ensemble ML models. Extensive experiments show that our DRT enhanced ensembles
can consistently achieve higher certified robustness than existing single and
ensemble ML models, demonstrating the state-of-the-art certified L2-robustness
on MNIST, CIFAR-10, and ImageNet datasets.