Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient
文献类型：期刊论文
| 作者 | Liang, Ling4; Hu, Xing3; Deng, Lei2; Wu, Yujie2; Li, Guoqi2; Ding, Yufei1; Li, Peng4; Xie, Yuan4 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2021-09-01 |
| 页码 | 15 |
| 关键词 | Spatiotemporal phenomena Computational modeling Perturbation methods Biological neural networks Backpropagation Unsupervised learning Training Adversarial attack backpropagation through time (BPTT) neuromorphic computing spike-compatible gradient spiking neural networks (SNNs) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2021.3106961 |
| 英文摘要 | Spiking neural network (SNN) is broadly deployed in neuromorphic devices to emulate brain function. In this context, SNN security becomes important while lacking in-depth investigation. To this end, we target the adversarial attack against SNNs and identify several challenges distinct from the artificial neural network (ANN) attack: 1) current adversarial attack is mainly based on gradient information that presents in a spatiotemporal pattern in SNNs, hard to obtain with conventional backpropagation algorithms; 2) the continuous gradient of the input is incompatible with the binary spiking input during gradient accumulation, hindering the generation of spike-based adversarial examples; and 3) the input gradient can be all-zeros (i.e., vanishing) sometimes due to the zero-dominant derivative of the firing function. Recently, backpropagation through time (BPTT)-inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given spatiotemporal gradient maps. We propose two approaches to address the above challenges of gradient-input incompatibility and gradient vanishing. Specifically, we design a gradient-to-spike (G2S) converter to convert continuous gradients to ternary ones compatible with spike inputs. Then, we design a restricted spike flipper (RSF) to construct ternary gradients that can randomly flip the spike inputs with a controllable turnover rate, when meeting all-zero gradients. Putting these methods together, we build an adversarial attack methodology for SNNs. Moreover, we analyze the influence of the training loss function and the firing threshold of the penultimate layer on the attack effectiveness. Extensive experiments are conducted to validate our solution. Besides the quantitative analysis of the influence factors, we also compare SNNs and ANNs against adversarial attacks under different attack methods. This work can help reveal what happens in SNN attacks and might stimulate more research on the security of SNN models and neuromorphic devices. |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000733549300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18012]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Deng, Lei |
| 作者单位 | 1.Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA 2.Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Precis Instrument, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 4.Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA |
| 推荐引用方式 GB/T 7714 | Liang, Ling,Hu, Xing,Deng, Lei,et al. Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15. |
| APA | Liang, Ling.,Hu, Xing.,Deng, Lei.,Wu, Yujie.,Li, Guoqi.,...&Xie, Yuan.(2021).Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15. |
| MLA | Liang, Ling,et al."Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15. |
入库方式： OAI收割
来源：计算技术研究所
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