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On the Certified Robustness for Ensemble Models and Beyond. (arXiv:2107.10873v2 [cs.LG] UPDATED)
April 22, 2022, 1:20 a.m. | Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, Bo Li
cs.CR updates on arXiv.org arxiv.org
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 …
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