DRE: density-based data selection with entropy for adversarial-robust deep learning models

Author:

Guo YuejunORCID,Hu Qiang,Cordy Maxime,Papadakis Michail,Le Traon Yves

Abstract

AbstractActive learning helps software developers reduce the labeling cost when building high-quality machine learning models. A core component of active learning is the acquisition function that determines which data should be selected to annotate.State-of-the-art (SOTA) acquisition functions focus on clean performance (e.g. accuracy) but disregard robustness (an important quality property), leading to fragile models with negligible robustness (less than 0.20%). In this paper, we first propose to integrate adversarial training into active learning (adversarial-robust active learning, ARAL) to produce robust models. Our empirical study on 11 acquisition functions and 15105 trained deep neural networks (DNNs) shows that ARAL can produce models with robustness ranging from 2.35% to 63.85%. Our study also reveals, however, that the acquisition functions that perform well on accuracy are worse than random sampling when it comes to robustness. Via examining the reasons behind this, we devise the density-based robust sampling with entropy (DRE) to target both clean performance and robustness. The core idea of DRE is to maintain a balance between selected data and the entire set based on the entropy density distribution. DRE outperforms SOTA functions in terms of robustness by up to 24.40%, while remaining competitive on accuracy. Additionally, the in-depth evaluation shows that DRE is applicable as a test selection metric for model retraining and stands out from all compared functions by up to 8.21% robustness.

Funder

Fonds National de la Recherche Luxembourg

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Active Code Learning: Benchmarking Sample-Efficient Training of Code Models;IEEE Transactions on Software Engineering;2024-05

2. Test Optimization in DNN Testing: A Survey;ACM Transactions on Software Engineering and Methodology;2024-04-20

3. Guiding the retraining of convolutional neural networks against adversarial inputs;PeerJ Computer Science;2023-08-08

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