{"title":"针对故障攻击的人工神经网络智能冗余方案","authors":"Troya Çağıl Köylü, S. Hamdioui, M. Taouil","doi":"10.1109/ETS54262.2022.9810380","DOIUrl":null,"url":null,"abstract":"Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.","PeriodicalId":334931,"journal":{"name":"2022 IEEE European Test Symposium (ETS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Smart Redundancy Schemes for ANNs Against Fault Attacks\",\"authors\":\"Troya Çağıl Köylü, S. Hamdioui, M. Taouil\",\"doi\":\"10.1109/ETS54262.2022.9810380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.\",\"PeriodicalId\":334931,\"journal\":{\"name\":\"2022 IEEE European Test Symposium (ETS)\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE European Test Symposium (ETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETS54262.2022.9810380\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE European Test Symposium (ETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETS54262.2022.9810380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smart Redundancy Schemes for ANNs Against Fault Attacks
Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.