Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2021

Abstract

Deep neural networks are vulnerable to adversarial attacks. Due to their black-box nature, it is rather challenging to interpret and properly repair these incorrect behaviors. This paper focuses on interpreting and repairing the incorrect behaviors of Recurrent Neural Networks (RNNs). We propose a lightweight model-based approach (RNNRepair) to help understand and repair incorrect behaviors of an RNN. Specifically, we build an influence model to characterize the stateful and statistical behaviors of an RNN over all the training data and to perform the influence analysis for the errors. Compared with the existing techniques on influence function, our method can efficiently estimate the influence of existing or newly added training samples for a given prediction at both sample level and segmentation level. Our empirical evaluation shows that the proposed influence model is able to extract accurate and understandable features. Based on the influence model, our proposed technique could effectively infer the influential instances from not only an entire testing sequence but also a segment within that sequence. Moreover, with the sample-level and segment-level influence relations, RNNRepair could further remediate two types of incorrect predictions at the sample level and segment level.

Discipline

Information Security | Software Engineering

Publication

Proceedings of the 38th International Conference on Machine Learning 2021: Virtual, July 18-24

Volume

139

First Page

11383

Last Page

11392

Publisher

PMLR

City or Country

Virtual Only

Additional URL

https://proceedings.mlr.press/v139/xie21b.html

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