基于属性定向测试的神经网络快速证伪
Fast Falsification of Neural Networks using Property Directed Testing
摘要：
神经网络现已广泛用于自主系统的感知、预测和控制。它们在安全关键系统中的部署提出了对此类网络的核查技术的需要。作为穷举和昂贵的验证算法的替代方案，轻量级伪造算法已被大量用于搜索产生不安全输出的系统输入，即，系统安全性的反例在这项工作中，我们提出了一个证伪算法的神经网络，指导搜索的一个反例，指导安全属性规范。我们的算法使用一个衍生物免费采样为基础的优化方法。我们评估我们的算法对45训练神经网络基准的ACAS徐系统对10个安全属性。我们表明，我们的伪造程序检测到所有的不安全的情况下，其他验证工具也报告为不安全的。此外，在性能方面，我们的证伪程序识别大多数不安全的实例更快，与最先进的前馈神经网络验证工具相比，如NNENUM和Neurify，在许多情况下，通过数量级。
英文摘要：
Neural networks are now extensively used in perception, prediction and control of autonomous systems. Their deployment in safety-critical systems brings forth the need for verification techniques for such networks. As an alternative to exhaustive and costly verification algorithms, lightweight falsification algorithms have been heavily used to search for an input to the system that produces an unsafe output, i.e., a counterexample to the safety of the system. In this work, we propose a falsification algorithm for neural networks that directs the search for a counterexample, guided by a safety property specification. Our algorithm uses a derivative-free sampling-based optimization method. We evaluate our algorithm on 45 trained neural network benchmarks of the ACAS Xu system against 10 safety properties. We show that our falsification procedure detects all the unsafe instances that other verification tools also report as unsafe. Moreover, in terms of performance, our falsification procedure identifies most of the unsafe instances faster, in comparison to the state-of-the-art verification tools for feed-forward neural networks such as NNENUM and Neurify and in many instances, by orders of magnitude.
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