With the rapid development of Artificial Intelligence (AI), the problem of AI security has gradually emerged. Most existing machine learning algorithms may be attacked by adversarial examples. An adversarial example is a slightly modified input sample that can lead to a false result of machine learning algorithms. This poses a potential security threat for many AI applications. Especially in the domain of robot path planning, the adversarial maps may result in multiple harmful effects on the predicted path. However, there is no suitable approach to automatically identify them. To our knowledge, all previous works used manual observation method to identify the attack results of adversarial maps, which is time-consuming. Aiming at the existing problems, this paper explores a method to automatically identify the adversarial examples in Value Iteration Networks (VIN), which has a strong generalization ability. We analyze the possible scenarios caused by the adversarial maps. We propose a training-based identification approach to VIN adversarial examples by combining the path feature comparison and path image classification. Experiments show that our method can achieve a high-accuracy and effective identification on VIN adversarial examples.

中文翻译：

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路径规划中 VIN 对抗性示例的基于训练的识别方法

随着人工智能（AI）的飞速发展，人工智能的安全问题也逐渐浮出水面。大多数现有的机器学习算法可能会受到对抗性示例的攻击。一个对抗性示例是一个稍微修改过的输入样本，它可能导致机器学习算法的错误结果。这对许多人工智能应用程序构成了潜在的安全威胁。特别是在机器人路径规划领域，对抗性地图可能会对预测路径产生多种有害影响。但是，没有合适的方法来自动识别它们。据我们所知，之前的所有工作都使用手动观察方法来识别对抗地图的攻击结果，这非常耗时。针对存在的问题，本文探索了一种在价值迭代网络（VIN）中自动识别对抗样本的方法，该方法具有很强的泛化能力。我们分析了对抗地图引起的可能场景。我们通过结合路径特征比较和路径图像分类，提出了一种基于训练的 VIN 对抗样本识别方法。实验表明，我们的方法可以实现对 VIN 对抗样本的高精度和有效识别。