DNN accelerators have been widely deployed in many scenarios to speed up the
inference process and reduce the energy consumption. One big concern about the
usage of the accelerators is the confidentiality of the deployed models: model
inference execution on the accelerators could leak side-channel information,
which enables an adversary to preciously recover the model details. Such model
extraction attacks can not only compromise the intellectual property of DNN
models, but also facilitate some adversarial attacks.
Although previous works have demonstrated a number of side-channel techniques
to extract models from DNN accelerators, they are not practical for two
reasons. (1) They only target simplified accelerator implementations, which
have limited practicality in the real world. (2) They require heavy human
analysis and domain knowledge. To overcome these limitations, this paper
presents Mercury, the first automated remote side-channel attack against the
off-the-shelf Nvidia DNN accelerator. The key insight of Mercury is to model
the side-channel extraction process as a sequence-to-sequence problem. The
adversary can leverage a time-to-digital converter (TDC) to remotely collect
the power trace of the target model's inference. Then he uses a learning model
to automatically recover the architecture details of the victim model from the
power trace without any prior knowledge. The adversary can further use the
attention mechanism to localize the leakage points that contribute most to the
attack. Evaluation results indicate that Mercury can keep the error rate of
model extraction below 1%.

Mercury 是首个对现成的 Nvidia DNN 加速器进行自动远程侧信道攻击的系统，通过将侧信道提取过程建模为序列到序列问题，利用时间到数字转换器（TDC）远程收集目标模型推理的功耗迹线，然后使用学习模型从功耗迹线中自动恢复受害模型的架构细节，进而使用注意力机制定位对攻击贡献最大的泄漏点，评估结果表明 Mercury 能够将模型提取的错误率保持在 1% 以下。

水银：一种用于 Nvidia 深度学习加速器的自动化远程侧信道攻击

Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning  Accelerator

Machine learning has become mainstream across industries. Numerous examples
proved the validity of it for security applications. In this work, we
investigate how to reverse engineer a neural network by using only power
side-channel information. To this end, we consider a multilayer perceptron as
the machine learning architecture of choice and assume a non-invasive and
eavesdropping attacker capable of measuring only passive side-channel leakages
like power consumption, electromagnetic radiation, and reaction time.
We conduct all experiments on real data and common neural net architectures
in order to properly assess the applicability and extendability of those
attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our
experiments show that the side-channel attacker is capable of obtaining the
following information: the activation functions used in the architecture, the
number of layers and neurons in the layers, the number of output classes, and
weights in the neural network. Thus, the attacker can effectively reverse
engineer the network using side-channel information.
Next, we show that once the attacker has the knowledge about the neural
network architecture, he/she could also recover the inputs to the network with
only a single-shot measurement. Finally, we discuss several mitigations one
could use to thwart such attacks.

该研究利用非侵入式的侧信道信息，针对多层感知器神经网络，研究了如何逆向工程并获取该网络的构架及权重， 并展示了恢复输入所需的可行性和应对该攻击的几种缓解方法。