Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters
IEMN CNRS-UMR8520（法国国家科学研究中心电子、微电子与纳米技术研究所 UMR8520）；Université Polytechnique Hauts-De-France（上法兰西理工大学）；Institute of Computer Engineering, Technische Universität Wien（维也纳技术大学计算机工程研究所）；New York University Abu Dhabi（纽约大学阿布扎比分校）
Rida El-Allami, Alberto Marchisio, Muhammad Shafique, Ihsen Alouani
备注：
Accepted for publication at the 24th Design, Automation and Test in Europe (DATE'21), February, 2021
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerability to adversarial attacks. In the quest for DL trustworthiness, recent works claimed the inherent robustness of Spiking Neural Networks (SNNs) to these attacks, without considering the variability in their structural spiking parameters. This paper explores the security enhancement of SNNs through internal structural parameters. Specifically, we investigate the SNNs robustness to adversarial attacks with different values of the neuron's firing voltage thresholds and time window boundaries. We thoroughly study SNNs security under different adversarial attacks in the strong white-box setting, with different noise budgets and under variable spiking parameters. Our results show a significant impact of the structural parameters on the SNNs' security, and promising sweet spots can be reached to design trustworthy SNNs with 85% higher robustness than a traditional non-spiking DL system. To the best of our knowledge, this is the first work that investigates the impact of structural parameters on SNNs robustness to adversarial attacks. The proposed contributions and the experimental framework is available online to the community for reproducible research.
TECNALIA；University of Deusto（德乌斯托大学）；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Auckland University of Technology（奥克兰理工大学）；Ulster University（阿尔斯特大学）；Bulgarian Academy of Sciences（保加利亚科学院）
Research around Spiking Neural Networks has ignited during the last years due to their advantages when compared to traditional neural networks, including their efficient processing and inherent ability to model complex temporal dynamics. Despite these differences, Spiking Neural Networks face similar issues than other neural computation counterparts when deployed in real-world settings. This work addresses one of the practical circumstances that can hinder the trustworthiness of this family of models: the possibility of querying a trained model with samples far from the distribution of its tra…
Xi’an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Peking University Health Science Center, Peking University（北京大学医学部）；Huawei Technologies Co Ltd.（华为技术有限公司）；Peng Cheng Laboratory（鹏城实验室）
FSTA-SNN:Frequency-based Spatial-Temporal Attention Module for Spiking Neural Networks
Zhejiang University-University of Illinois Urbana Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校研究院）；The College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
Huazhong University of Science and Technology（华中科技大学）
Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model. Due to the spatiotemporal characteristics, novel data augmentations are required to process the unconventional visual signals of these cameras. In this paper, we propose a novel Event SpatioTemporal Fragments (ESTF) augmentation method. It preserves the continuity of neuromorphic data by drifting or inverting fragments of the spatiotemporal event stream to simulate the disturbance of brightness variations, leading to more r…
SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention
School of Astronautics, Harbin Institute of Technology（哈尔滨工业大学航天学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；HebeiGejun Science & Technology Company（河北格钧科技公司）；School of Computer Science & Statistics, Trinity College Dublin（都柏林三一学院计算机与统计学院）
Technische Universität Wien（维也纳工业大学）；Politecnico di Torino（都灵理工大学）；Université Polytechnique Hauts-De-France（上法兰西理工大学）
Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injecti…
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science, Zhejiang University of Technology（浙江工业大学计算机学院）
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；ZJU-UIUC Institute, Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合学院）
The main computational task of Scientific Machine Learning (SciML) is function regression, required both for inputs as well as outputs of a simulation. Physics-Informed Neural Networks (PINNs) and neural operators (such as DeepONet) have been very effective in solving Partial Differential Equations (PDEs), but they tax computational resources heavily and cannot be readily adopted for edge computing. Here, we address this issue by considering Spiking Neural Networks (SNNs), which have shown promise in reducing energy consumption by two orders of magnitude or more. We present a SNN-based method …
Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence processing of binary information in SNNs. To address this, in this paper, we introduce a novel Neural Architecture Search (NAS) approach for finding better SNN architectures. Inspired by recent NAS approaches that find the optimal architecture from…
The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) are run on neuromorphic processors, is regarded as an energy-efficient and robust alternative to the state-of-the-art real-time robotic controllers for low dimensional control tasks. The challenge now for this new computing paradigm is to scale so that it can keep up with real-wo…
Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Peking University（北京大学）；Key Laboratory of Machine Perception (Ministry of Education), Peking University（北京大学机器感知重点实验室（教育部））；National Key Laboratory of General Artificial Intelligence（通用人工智能全国重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware
Kirchhoff-Institute for Physics, Heidelberg University（海德堡大学基尔霍夫物理研究所）；Department of Physiology, University of Bern（伯尔尼大学生理学系）；Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）
