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Architecture-Preserving Provable Repair of Deep Neural Networks

Authors:

Stephanie Nawas,

Jacqueline Mitchell,

Aditya V. ThakurAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 7, Issue PLDI

Article No.: 124, Pages 443 - 467

https://doi.org/10.1145/3591238

Published: 06 June 2023 Publication History

Abstract

Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image recognition. However, DNNs are far from infallible, and incorrect behavior of DNNs can have disastrous real-world consequences. This paper addresses the problem of architecture-preserving V-polytope provable repair of DNNs. A V-polytope defines a convex bounded polytope using its vertex representation. V-polytope provable repair guarantees that the repaired DNN satisfies the given specification on the infinite set of points in the given V-polytope. An architecture-preserving repair only modifies the parameters of the DNN, without modifying its architecture. The repair has the flexibility to modify multiple layers of the DNN, and runs in polynomial time. It supports DNNs with activation functions that have some linear pieces, as well as fully-connected, convolutional, pooling and residual layers. To the best our knowledge, this is the first provable repair approach that has all of these features. We implement our approach in a tool called APRNN. Using MNIST, ImageNet, and ACAS Xu DNNs, we show that it has better efficiency, scalability, and generalization compared to PRDNN and REASSURE, prior provable repair methods that are not architecture preserving.

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Cited By

Aanjaneya MNagarakatte S(2024)Maximum Consensus Floating Point Solutions for Infeasible Low-Dimensional Linear Programs with Convex Hull as the Intermediate RepresentationProceedings of the ACM on Programming Languages10.1145/36564278:PLDI(1239-1263)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656427
Nawas STao ZThakur A(2024)Provable Repair of Vision TransformersAI Verification10.1007/978-3-031-65112-0_8(156-178)Online publication date: 22-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-65112-0_8
Manzanas Lopez DJohnson T(2023)Empirical Analysis of Benchmark Generation for the Verification of Neural Network Image ClassifiersBridging the Gap Between AI and Reality10.1007/978-3-031-46002-9_21(331-347)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-46002-9_21

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Architecture-Preserving Provable Repair of Deep Neural Networks

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 7, Issue PLDI

June 2023

2020 pages

EISSN:2475-1421

DOI:10.1145/3554310

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Michael Hicks
Amazon, USA

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Published: 06 June 2023

Published in PACMPL Volume 7, Issue PLDI

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Cited By

Aanjaneya MNagarakatte S(2024)Maximum Consensus Floating Point Solutions for Infeasible Low-Dimensional Linear Programs with Convex Hull as the Intermediate RepresentationProceedings of the ACM on Programming Languages10.1145/36564278:PLDI(1239-1263)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656427
Nawas STao ZThakur A(2024)Provable Repair of Vision TransformersAI Verification10.1007/978-3-031-65112-0_8(156-178)Online publication date: 22-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-65112-0_8
Manzanas Lopez DJohnson T(2023)Empirical Analysis of Benchmark Generation for the Verification of Neural Network Image ClassifiersBridging the Gap Between AI and Reality10.1007/978-3-031-46002-9_21(331-347)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-46002-9_21

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