Training for faster adversarial robustness verification via inducing Relu stability

We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models...

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Detalhes bibliográficos
Principais autores: Xiao, Kai Yuanqing, Tjeng, Vincent, Shafiullah, Nur Muhammad Mahi., Mądry, Aleksander
Outros Autores: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Formato: Artigo
Idioma:English
Publicado em: ICLR 2021
Acesso em linha:https://hdl.handle.net/1721.1/130110