Towards General Robustness Verification of MaxPool-Based Convolutional Neural Networks via Tightening Linear Approximation

Yuan Xiao, Shiqing Ma, Juan Zhai, Chunrong Fang, Jinyuan Jia, Zhenyu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The robustness of convolutional neural networks (CNNs) is vital to modern AI-driven systems. It can be quanti-fied by formal verification by providing a certified lower bound, within which any perturbation does not alter the original input's classification result. It is challenging due to nonlinear components, such as MaxPool. At present, many verification methods are sound but risk losing some precision to enhance efficiency and scalability, and thus, a certified lower bound is a crucial criterion for evaluating the performance of verification tools. In this paper, we present MaxLin, a robustness verifier for MaxPool-based CNNs with tight Linear approximation. By tight-ening the linear approximation of the MaxPool function, we can certify larger certified lower bounds of CNNs. We evaluate MaxLin with open-sourced benchmarks, including LeNet and networks trained on the MNIST, CIFAR-10, and Tiny ImageNet datasets. The results show that MaxLin outperforms state-of-the-art tools with up to 110.60% improvement regarding the certified lower bound and 5.13 × speedup for the same neural networks. Our code is available at https://github.com/xiaoyuanpigo/maxlin.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
PublisherIEEE Computer Society
Pages24766-24775
Number of pages10
ISBN (Electronic)9798350353006
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States
Duration: Jun 16 2024Jun 22 2024

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
Country/TerritoryUnited States
CitySeattle
Period6/16/246/22/24

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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