Global Robustness Verification Networks

The wide deployment of deep neural networks, though achieving great success in many domains, has severe safety and reliability concerns. Existing adversarial attack generation and automatic verification techniques cannot formally verify whether a network is globally robust, i.e., the absence or not...

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Bibliographic Details
Published inarXiv.org
Main Authors Sun, Weidi, Lu, Yuteng, Zhang, Xiyue, Zhu, Zhanxing, Sun, Meng
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.06.2020
Subjects
Artificial neural networks
Back propagation
Computer architecture
Robustness
Software-defined networking
Verification
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Summary:The wide deployment of deep neural networks, though achieving great success in many domains, has severe safety and reliability concerns. Existing adversarial attack generation and automatic verification techniques cannot formally verify whether a network is globally robust, i.e., the absence or not of adversarial examples in the input space. To address this problem, we develop a global robustness verification framework with three components: 1) a novel rule-based ``back-propagation'' finding which input region is responsible for the class assignment by logic reasoning; 2) a new network architecture Sliding Door Network (SDN) enabling feasible rule-based ``back-propagation''; 3) a region-based global robustness verification (RGRV) approach. Moreover, we demonstrate the effectiveness of our approach on both synthetic and real datasets.
ISSN:2331-8422