Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Capital Normal University（首都师范大学）
Hao Lv, Bing Li, Lei Zhang, Cheng Liu, Ying Wang
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submitted to IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
The RRAM-based neuromorphic computing system has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and thus being widely used in many data-centric applications. The reliability and security issues of the NCS therefore become an essential problem. In this paper, we systematically investigated the adversarial threats to the RRAM-based NCS and observed that the RRAM hardware feature can be leveraged to strengthen the attack effect, which has not been granted sufficient attention by previous algorithmic attack methods. Thus, we proposed two types of hardware-aware attack methods with respect to different attack scenarios and objectives. The first is adversarial attack, VADER, which perturbs the input samples to mislead the prediction of neural networks. The second is fault injection attack, EFI, which perturbs the network parameter space such that a specified sample will be classified to a target label, while maintaining the prediction accuracy on other samples. Both attack methods leverage the RRAM properties to improve the performance compared with the conventional attack methods. Experimental results show that our hardware-aware attack methods can achieve nearly 100% attack success rate with extremely low operational cost, while maintaining the attack stealthiness.
Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey
Faculty of Science and Engineering, University of Groningen（科学与工程学院，格罗宁根大学）；Faculty of Electrical Engineering and Computing, University of Zagreb（电气工程与计算学院，扎格reb大学）；Radboud University（拉德堡德大学）；Centre for Secure Information Technologies (CSIT), Queen's University Belfast（安全信息科技中心（CSIT），女王大学贝尔法斯特分校）；Faculty of Computer Science and Mathematics, University of Passau（计算机科学与数学学院，帕绍大学）；University of Amsterdam（阿姆斯特丹大学）；Digital Security (DiS) Group, Radboud University（数字安全（DiS）小组，拉德堡德大学）
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors
Technische Universität Wien（维也纳工业大学）；Politecnico di Torino（都灵理工大学）；New York University（纽约大学）
