Jung-Shian Li, Yen-Chu Peng, I. Liu, Chuan-Gang Liu
{"title":"一种新的深度神经网络投毒攻击","authors":"Jung-Shian Li, Yen-Chu Peng, I. Liu, Chuan-Gang Liu","doi":"10.1145/3545729.3545736","DOIUrl":null,"url":null,"abstract":"In healthcare field, many machine learning schemes have been applied in analyzing image content dataset. Among them, deep neural networks (DNNs), also known as deep learning, catches much attention. However, if deep neural networks are compromised by the attacker, medical diagnosis may be wrong, which leads to vital result. Recently, we find a new poisoning attack on DNNs may possibly happens due to poisoning dataset. This new poisoning attack, Category Diverse attack, has better ability to paralyze DNNs. Our performance experiments show our Category diverse attack actually leads to large accuracy drop of DNNs. We hope this discovery can help the information experts can improve the medical dataset quality in the future.","PeriodicalId":432782,"journal":{"name":"Proceedings of the 6th International Conference on Medical and Health Informatics","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Poisoning Attacks on Deep Neural Networks\",\"authors\":\"Jung-Shian Li, Yen-Chu Peng, I. Liu, Chuan-Gang Liu\",\"doi\":\"10.1145/3545729.3545736\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In healthcare field, many machine learning schemes have been applied in analyzing image content dataset. Among them, deep neural networks (DNNs), also known as deep learning, catches much attention. However, if deep neural networks are compromised by the attacker, medical diagnosis may be wrong, which leads to vital result. Recently, we find a new poisoning attack on DNNs may possibly happens due to poisoning dataset. This new poisoning attack, Category Diverse attack, has better ability to paralyze DNNs. Our performance experiments show our Category diverse attack actually leads to large accuracy drop of DNNs. We hope this discovery can help the information experts can improve the medical dataset quality in the future.\",\"PeriodicalId\":432782,\"journal\":{\"name\":\"Proceedings of the 6th International Conference on Medical and Health Informatics\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th International Conference on Medical and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3545729.3545736\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Medical and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3545729.3545736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In healthcare field, many machine learning schemes have been applied in analyzing image content dataset. Among them, deep neural networks (DNNs), also known as deep learning, catches much attention. However, if deep neural networks are compromised by the attacker, medical diagnosis may be wrong, which leads to vital result. Recently, we find a new poisoning attack on DNNs may possibly happens due to poisoning dataset. This new poisoning attack, Category Diverse attack, has better ability to paralyze DNNs. Our performance experiments show our Category diverse attack actually leads to large accuracy drop of DNNs. We hope this discovery can help the information experts can improve the medical dataset quality in the future.