Reachability Analysis of Convolutional Neural Networks

• URL: http://arxiv.org/abs/2106.12074v1
• Date: Tue, 22 Jun 2021 21:42:00 GMT
• Title: Reachability Analysis of Convolutional Neural Networks
• Authors: Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T Johnson, Danil Prokhorov
• Abstract summary: We propose an approach to compute the exact reachable sets of a network given an input domain. Our approach is also capable of backtracking to the input domain given an output reachable set. An approach for fast analysis is also introduced, which conducts fast computation of reachable sets.
• Score: 10.384532888747993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks have been widely employed as an effective technique to handle complex and practical problems. However, one of the fundamental problems is the lack of formal methods to analyze their behavior. To address this challenge, we propose an approach to compute the exact reachable sets of a network given an input domain, where the reachable set is represented by the face lattice structure. Besides the computation of reachable sets, our approach is also capable of backtracking to the input domain given an output reachable set. Therefore, a full analysis of a network's behavior can be realized. In addition, an approach for fast analysis is also introduced, which conducts fast computation of reachable sets by considering selected sensitive neurons in each layer. The exact pixel-level reachability analysis method is evaluated on a CNN for the CIFAR10 dataset and compared to related works. The fast analysis method is evaluated over a CNN CIFAR10 dataset and VGG16 architecture for the ImageNet dataset.

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