Software development in the aerospace domain requires adhering to strict, high-quality standards. While there exist regulatory guidelines for commercial software in this domain (e.g., ARP-4754 and DO-178), these do not apply to software with deep neural network (DNN) components. Consequently, it is unclear how to allow aerospace systems to benefit from the deep learning revolution. Our work here seeks to address this challenge with a novel, output-centric approach for DNN certification. Our method employs statistical verification techniques, and has the key advantage of being able to flag specific inputs for which the DNN's output may be unreliable - so that they may be later inspected by a human expert. To achieve this, our method conducts a statistical analysis of the DNN's predictions for other, nearby inputs, in order to detect inconsistencies. This is in contrast to existing techniques, which typically attempt to certify the entire DNN, as opposed to individual outputs. Our method uses the DNN as a black-box, and makes no assumptions about its topology. We hope that this work constitutes another step towards integrating DNNs in safety-critical applications - especially in the aerospace domain, where high standards of quality and reliability are crucial.

翻译：航空航天领域的软件开发需遵循严格的高质量标准。尽管该领域存在针对商业软件的监管指南（如ARP-4754和DO-178），但这些指南并不适用于包含深度神经网络（DNN）组件的软件。因此，如何让航空航天系统受益于深度学习革命尚不明确。本研究旨在通过一种新颖的、以输出为中心的DNN认证方法应对这一挑战。该方法采用统计验证技术，其关键优势在于能够标记DNN输出可能不可靠的特定输入，从而可供后续人类专家检查。为实现此目标，该方法对DNN在邻近输入上的预测进行统计分析，以检测不一致性。这不同于现有技术——后者通常试图认证整个DNN而非单个输出。该方法将DNN视为黑箱，且不对其拓扑结构做任何假设。我们期望这项工作能成为将DNN集成至安全关键应用（尤其在质量与可靠性要求极高的航空航天领域）的又一推动力。