In recent years, on-device deep learning has gained attention as a means of
developing affordable deep learning applications for mobile devices. However,
on-device models are constrained by limited energy and computation resources.
In the mean time, a poisoning attack known as sponge poisoning has been
developed.This attack involves feeding the model with poisoned examples to
increase the energy consumption during inference. As previous work is focusing
on server hardware accelerators, in this work, we extend the sponge poisoning
attack to an on-device scenario to evaluate the vulnerability of mobile device
processors. We present an on-device sponge poisoning attack pipeline to
simulate the streaming and consistent inference scenario to bridge the
knowledge gap in the on-device setting. Our exclusive experimental analysis
with processors and on-device networks shows that sponge poisoning attacks can
effectively pollute the modern processor with its built-in accelerator. We
analyze the impact of different factors in the sponge poisoning algorithm and
highlight the need for improved defense mechanisms to prevent such attacks on
on-device deep learning applications.

本文针对移动设备处理器开展了基于海绵毒化攻击的深度学习攻击，并通过实验发现其可以有效地污染现代处理器的内置加速器，并分析了海绵毒化算法中不同因素的影响，并强调了需要改进防御机制以防止此类攻击。