Poster：Sponge ML移动应用模型攻击
Poster: Sponge ML Model Attacks of Mobile Apps
摘要：
机器学习（ML）驱动的应用程序用于手机、平板电脑、智能手表和物联网设备等普及设备。协作分布式机器学习的最新进展，如联邦学习（FL），试图解决用户和数据所有者的隐私问题，因此被谷歌、Facebook和苹果等科技行业领导者所使用。然而，FL系统和模型仍然容易受到敌对成员和属性推断以及模型中毒攻击的影响，特别是在最近提出的FL-as-a-Service生态系统中，这可以使攻击者访问多个ML驱动的应用程序。在这项工作中，我们专注于最近提出的Sponge攻击：它旨在吸收执行ML模型推理（而不是训练）时消耗的能量，而不会妨碍分类器的性能。最近的研究表明，对支持ASCI的GPU的海绵攻击可能会增加功耗和推理时间。在这项工作中，我们第一次调查了移动环境中的这种攻击，并测量了它对移动设备上应用程序内部运行的ML模型的影响。
英文摘要：
Machine Learning (ML)-powered apps are used in pervasive devices such as phones, tablets, smartwatches and IoT devices. Recent advances in collaborative, distributed ML such as Federated Learning (FL) attempt to solve privacy concerns of users and data owners, and thus used by tech industry leaders such as Google, Facebook and Apple. However, FL systems and models are still vulnerable to adversarial membership and attribute inferences and model poisoning attacks, especially in FL-as-a-Service ecosystems recently proposed, which can enable attackers to access multiple ML-powered apps. In this work, we focus on the recently proposed Sponge attack: It is designed to soak up energy consumed while executing inference (not training) of ML model, without hampering the classifier's performance. Recent work has shown sponge attacks on ASCI-enabled GPUs can potentially escalate the power consumption and inference time. For the first time, in this work, we investigate this attack in the mobile setting and measure the effect it can have on ML models running inside apps on mobile devices.
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