With the rapid development of the Internet, the nextgeneration network (5G) has emerged. 5G can support a variety of new applications, such as the Internet of Things (IoT), virtual reality (VR), and the Internet of Vehicles. Most of these new applications depend on deep learning algorithms, which have made great advances in many areas of artificial intelligence (AI). However, researchers have found that AI algorithms based on deep learning pose numerous security problems. For example, deep learning is susceptible to a well-designed input sample formed by adding small perturbations to the original sample. This well-designed input with small perturbations, which are imperceptible to humans, is called an adversarial example. An adversarial example is similar to a truth example, but it can render the deep learning model invalid. In this article, we generate adversarial examples for spatiotemporal data. Based on the travel time estimation (TTE) task, we use two methods-white-box and blackbox attacks-to invalidate deep learning models. Experiment results show that the adversarial examples successfully attack the deep learning model and thus that AI security is a big challenge of 5G.

中文翻译：

---

5G 网络中的人工智能安全：估计旅行时间任务的对抗性示例

随着互联网的飞速发展，下一代网络（5G）应运而生。5G可以支持物联网（IoT）、虚拟现实（VR）、车联网等多种新应用。这些新应用大多依赖于深度学习算法，这些算法在人工智能 (AI) 的许多领域都取得了巨大进步。然而，研究人员发现基于深度学习的 AI 算法会带来许多安全问题。例如，深度学习容易受到通过向原始样本添加小扰动而形成的精心设计的输入样本的影响。这种精心设计的具有人类无法察觉的小扰动的输入被称为对抗性示例。对抗样本类似于真实样本，但它会使深度学习模型无效。在本文中，我们为时空数据生成对抗样本。基于旅行时间估计 (TTE) 任务，我们使用白盒攻击和黑盒攻击两种方法来使深度学习模型无效。实验结果表明，对抗样本成功攻击了深度学习模型，因此人工智能安全是 5G 的一大挑战。