research-article

VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks

Authors:

Cheng-Chao Huang,

Zhen WangAuthors Info & Claims

ICSE '24: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering

Article No.: 8, Pages 1 - 13

https://doi.org/10.1145/3597503.3623332

Published: 06 February 2024 Publication History

Abstract

Neural network repair aims to fix the 'bugs'¹ of neural networks by modifying the model's architecture or parameters. However, due to the data-driven nature of neural networks, it is difficult to explain the relationship between the internal neurons and erroneous behaviors, making further repair challenging. While several work exists to identify responsible neurons based on gradient or causality analysis, their effectiveness heavily rely on the quality of available 'bugged' data and multiple heuristics in layer or neuron selection. In this work, we address the issue utilizing the power of formal verification (in particular for neural networks). Specifically, we propose VeRe, a verification-guided neural network repair framework that performs fault localization based on linear relaxation to symbolically calculate the repair significance of neurons and furthermore optimize the parameters of problematic neurons to repair erroneous behaviors. We evaluated VeRe on various repair tasks, and our experimental results show that VeRe can efficiently and effectively repair all neural networks without degrading the model's performance. For the task of removing backdoors, VeRe successfully reduces attack success rate from 98.47% to 0.38% on average, while causing an average performance drop of 0.9%. For the task of repairing safety properties, VeRe successfully repairs all the 36 tasks and achieves 99.87% generalization on average.

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Cited By

Sun XLiu WWang SChen TTao YMao X(2024)AutoRIC: Automated Neural Network Repairing Based on Constrained OptimizationACM Transactions on Software Engineering and Methodology10.1145/3690634Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3690634
Chen ZZhou JSun YWang JXuan QYang XChristakis MPradel M(2024)Interpretability Based Neural Network RepairProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680330(908-919)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680330

Index Terms

VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks
1. Computing methodologies
  1. Artificial intelligence
2. Security and privacy
  1. Software and application security

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ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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DOI:10.1145/3597503

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Cited By

Sun XLiu WWang SChen TTao YMao X(2024)AutoRIC: Automated Neural Network Repairing Based on Constrained OptimizationACM Transactions on Software Engineering and Methodology10.1145/3690634Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3690634
Chen ZZhou JSun YWang JXuan QYang XChristakis MPradel M(2024)Interpretability Based Neural Network RepairProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680330(908-919)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680330

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