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Freeze-and-mutate: abnormal sample identification for DL applications through model core analysis by Huiyan Wang; Ziqi Chen; Chang Xu is a Computer Science article available to read on EtoBox.

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Deep learning (DL) applications, representing an emerging form of new software, are gaining increasing popularity by their intelligent and adaptive services. However, their service reliability depends highly on the prediction accuracy of their internallyintegrated DL models. In practice, DL models are often observed to suffer from ill predictions upon abnormal inputs (e.g., adversarial attacking samples, out-of-distribution (OOD) samples, and etc.), and this could easily lead to unexpected behaviors or even catastrophic consequences (e.g., system crash). One promising way to guard the application reliability is to reveal such abnormal inputs in time before they are fed to the DL models integrated in the concerned applications. Then remedy actions (e.g., discarding or fixing these inputs) can be done to protect applications from acting abnormally. Existing work addressed this revealing problem by either making sample distance-comparison based analysis or generating sufficient model mutants for comparative analysis. However, such treatments caused a restricted focus on samples only, while overlooking the DL models themselves, or had to analyze massive mutants, incurring non-negligibl

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Author

Huiyan Wang; Ziqi Chen; Chang Xu

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)