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Title: An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks
Convolutional neural networks (CNNs) have achieved immense popularity in areas like computer vision, image processing, speech proccessing, and many others. Unfortunately, despite their excellent performance, they are prone to producing erroneous results — for example, minor perturbations to their inputs can result in severe classification errors. In this paper, we present the CNN-ABS framework, which implements an abstraction-refinement based scheme for CNN verification. Specifically, CNN-ABS simplifies the verification problem through the removal of convolutional connections in a way that soundly creates an over-approximation of the original problem; it then iteratively restores these connections if the resulting problem becomes too abstract. CNN-ABS is designed to use existing verification engines as a backend, and our evaluation demonstrates that it can significantly boost the performance of a state-of-the-art DNN verification engine, reducing runtime by 15.7% on average.  more » « less
Award ID(s):
1814369
PAR ID:
10397533
Author(s) / Creator(s):
; ;
Editor(s):
Bouajjani, Ahmed; Holk, Lukas; Wu, Zhilin
Date Published:
Journal Name:
Proceedings of the 20th International Symposium on Automated Technology for Verification and Analysis (ATVA)
Volume:
13505
Page Range / eLocation ID:
391-396
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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