Machine learning (ML) algorithms are increasingly being integrated into embedded and IoT systems that surround us, and they are vulnerable to adversarial attacks. The deployment of these ML algorithms on resource-limited embedded platforms also requires the use of model compression techniques. The impact of such model compression techniques on adversarial robustness in ML is an important and emerging area of research. This article provides an overview of the landscape of adversarial attacks and ML model compression techniques relevant to embedded systems. We then describe efforts that seek to understand the relationship between adversarial attacks and ML model compression before discussing open problems in this area.

翻译：机器学习算法正日益集成到我们周围的嵌入式系统和物联网设备中，但这些算法容易受到对抗攻击。在资源受限的嵌入式平台上部署这些机器学习算法时，还需要采用模型压缩技术。此类模型压缩技术对机器学习对抗鲁棒性的影响，是一个重要且新兴的研究领域。本文综述了与嵌入式系统相关的对抗攻击和机器学习模型压缩技术的整体现状。随后，我们描述了旨在理解对抗攻击与机器学习模型压缩之间关系的相关研究，并进一步探讨了该领域的开放性问题。