一句话：提出 Adapt，通过持续自适应地调整神经元选择策略，显著提升深度神经网络白盒测试的覆盖率与对抗输入发现能力。
随着深度神经网络越来越多地用于安全攸关场景，系统化地测试其行为已成为关键问题，近年也出现了多种神经网络测试技术，但总体仍处于早期阶段，现有方法效果不够理想。本文聚焦白盒测试方向，指出并试图解决当前最优方法的一个关键局限：即所谓的神经元选择策略（neuron-selection strategy）是白盒测试的核心组件，而既有方法并未充分发挥其作用。为此作者提出 Adapt，在测试过程中不断自适应地调整神经元选择策略，使其与正在进行的测试过程相匹配。在真实网络模型与数据集上的实验表明，Adapt 在覆盖率和所发现的对抗输入数量两方面都明显优于现有测试技术。
We present Adapt, a new white-box testing technique for deep neural networks. As deep neural networks are increasingly used in safety-first applications, testing their behavior systematically has become a critical problem. Accordingly, various testing techniques for deep neural networks have been proposed in recent years. However, neural network testing is still at an early stage and existing techniques are not yet sufficiently effective. In this paper, we aim to advance this field, in particular white-box testing approaches for neural networks, by identifying and addressing a key limitation of existing state-of-the-arts. We observe that the so-called neuron-selection strategy is a critical component of white-box testing and propose a new technique that effectively employs the strategy by continuously adapting it to the ongoing testing process. Experiments with real-world network models and datasets show that Adapt is remarkably more effective than existing testing techniques in terms of coverage and adversarial inputs found.
DOI 原文 ·
@inproceedings{paperbot6079,
title = {Effective white-box testing of deep neural networks with adaptive neuron-selection strategy},
author = {Seokhyun Lee and Sooyoung Cha and Dain Lee and Hakjoo Oh},
booktitle = {ISSTA},
year = {2020},
doi = {10.1145/3395363.3397346}
}