This paper investigates the vulnerability of spiking neural networks (SNNs)
and federated learning (FL) to backdoor attacks using neuromorphic data.
Despite the efficiency of SNNs and the privacy advantages of FL, particularly
in low-powered devices, we demonstrate that these systems are susceptible to
such attacks. We first assess the viability of using FL with SNNs using
neuromorphic data, showing its potential usage. Then, we evaluate the
transferability of known FL attack methods to SNNs, finding that these lead to
suboptimal attack performance. Therefore, we explore backdoor attacks involving
single and multiple attackers to improve the attack performance. Our primary
contribution is developing a novel attack strategy tailored to SNNs and FL,
which distributes the backdoor trigger temporally and across malicious devices,
enhancing the attack's effectiveness and stealthiness. In the best case, we
achieve a 100 attack success rate, 0.13 MSE, and 98.9 SSIM. Moreover, we adapt
and evaluate an existing defense against backdoor attacks, revealing its
inadequacy in protecting SNNs. This study underscores the need for robust
security measures in deploying SNNs and FL, particularly in the context of
backdoor attacks.

该研究探讨了脉冲神经网络（SNNs）和联邦学习（FL）在使用神经形态数据时受到后门攻击的脆弱性。研究发现，尽管 SNNs 的效率和 FL 的隐私优势使其在低功率设备上具有潜力，但这些系统容易受到此类攻击。研究采用了针对 SNNs 和 FL 定制的新型攻击策略，通过在时间和恶意设备之间分布后门触发器来增强攻击的有效性和隐蔽性，进一步证明了在部署 SNNs 和 FL 时需要稳健的安全措施。

时间分布的后门攻击对联邦脉冲学习的影响

Time-Distributed Backdoor Attacks on Federated Spiking Learning

Recognizing faces and their underlying emotions is an important aspect of
biometrics. In fact, estimating emotional states from faces has been tackled
from several angles in the literature. In this paper, we follow the novel route
of using neuromorphic data to predict valence and arousal values from faces.
Due to the difficulty of gathering event-based annotated videos, we leverage an
event camera simulator to create the neuromorphic counterpart of an existing
RGB dataset. We demonstrate that not only training models on simulated data can
still yield state-of-the-art results in valence-arousal estimation, but also
that our trained models can be directly applied to real data without further
training to address the downstream task of emotion recognition. In the paper we
propose several alternative models to solve the task, both frame-based and
video-based.

使用神经形态学数据从面部预测价值和唤醒值，并将训练模型直接应用于情绪识别任务。

神经形态学价值和唤起估计

Neuromorphic Valence and Arousal Estimation

Neuromorphic data, recording frameless spike events, have attracted
considerable attention for the spatiotemporal information components and the
event-driven processing fashion. Spiking neural networks (SNNs) represent a
family of event-driven models with spatiotemporal dynamics for neuromorphic
computing, which are widely benchmarked on neuromorphic data. Interestingly,
researchers in the machine learning community can argue that recurrent
(artificial) neural networks (RNNs) also have the capability to extract
spatiotemporal features although they are not event-driven. Thus, the question
of "what will happen if we benchmark these two kinds of models together on
neuromorphic data" comes out but remains unclear. In this work, we make a
systematic study to compare SNNs and RNNs on neuromorphic data, taking the
vision datasets as a case study. First, we identify the similarities and
differences between SNNs and RNNs (including the vanilla RNNs and LSTM) from
the modeling and learning perspectives. To improve comparability and fairness,
we unify the supervised learning algorithm based on backpropagation through
time (BPTT), the loss function exploiting the outputs at all timesteps, the
network structure with stacked fully-connected or convolutional layers, and the
hyper-parameters during training. Especially, given the mainstream loss
function used in RNNs, we modify it inspired by the rate coding scheme to
approach that of SNNs. Furthermore, we tune the temporal resolution of datasets
to test model robustness and generalization. At last, a series of contrast
experiments are conducted on two types of neuromorphic datasets: DVS-converted
(N-MNIST) and DVS-captured (DVS Gesture).

本研究对 SNN 和 RNN（包括 Vanilla RNN 和 LSTM）进行了对比研究，并通过对视觉数据集进行实验，创新性地改进了 RNN 的丢失函数和调整了数据集的时间分辨率以测试模型的鲁棒性和概括能力。