Evolving robust neural architectures to defend from adversarial attacks

研究成果: ジャーナルへの寄稿会議記事査読

1 被引用数 (Scopus)


Neural networks are prone to misclassify slightly modified input images. Recently, many defences have been proposed, but none have improved the robustness of neural networks consistently. Here, we propose to use adversarial attacks as a function evaluation to search for neural architectures that can resist such attacks automatically. Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures. A significant reason for this lies in their limited search space. By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of concatenation layers in the search, we were able to evolve an architecture that is inherently accurate on adversarial samples. Interestingly, this inherent robustness of the evolved architecture rivals state-ofthe-art defences such as adversarial training while being trained only on the non-adversarial samples. Moreover, the evolved architecture makes use of some peculiar traits which might be useful for developing even more robust ones. Thus, the results here confirm that more robust architectures exist as well as opens up a new realm of feasibilities for the development and exploration of neural networks.

ジャーナルCEUR Workshop Proceedings
出版ステータス出版済み - 2020
イベント2020 Workshop on Artificial Intelligence Safety, AISafety 2020 - Yokohama, 日本
継続期間: 1月 5 20211月 10 2021

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)


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