AI系统的鲁棒性测试：交通标志识别案例研究
Robustness testing of AI systems: A case study for traffic sign recognition
摘要：
在过去的几年里，人工智能系统，特别是神经网络，在性能上有了巨大的提高，它们现在被广泛应用。与经典的符号AI系统不同，神经网络是使用大型数据集训练的，其内部结构可能包含数十亿个参数，不适合人类解释。因此，到目前为止，如果神经网络处理的输入数据与训练期间看到的输入数据显著不同，则在操作期间为神经网络的正确行为提供广泛的保证是不可行的。然而，人工智能系统的许多应用都是安全或安全关键的，因此需要在面对意外事件时获得关于系统鲁棒性的声明，无论这些事件是自然发生的还是由攻击者以有针对性的方式引起的。作为为此类应用开发鲁棒AI系统的一步，本文介绍了如何实际检查AI系统的鲁棒性，以及可以使用哪些方法和指标来进行检查。鲁棒性测试方法的描述和分析自动驾驶中的交通标志识别的示例用例。
英文摘要：
In the last years, AI systems, in particular neural networks, have seen a tremendous increase in performance, and they are now used in a broad range of applications. Unlike classical symbolic AI systems, neural networks are trained using large data sets and their inner structure containing possibly billions of parameters does not lend itself to human interpretation. As a consequence, it is so far not feasible to provide broad guarantees for the correct behaviour of neural networks during operation if they process input data that significantly differ from those seen during training. However, many applications of AI systems are security- or safety-critical, and hence require obtaining statements on the robustness of the systems when facing unexpected events, whether they occur naturally or are induced by an attacker in a targeted way. As a step towards developing robust AI systems for such applications, this paper presents how the robustness of AI systems can be practically examined and which methods and metrics can be used to do so. The robustness testing methodology is described and analysed for the example use case of traffic sign recognition in autonomous driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
