Jingsheng Qian;Hangjie Yi;Honggang Liu;Xuanyu Jin;Wanzeng Kong
{"title":"基于查询效率和低失真的脑印识别黑盒攻击","authors":"Jingsheng Qian;Hangjie Yi;Honggang Liu;Xuanyu Jin;Wanzeng Kong","doi":"10.1109/LSP.2025.3563446","DOIUrl":null,"url":null,"abstract":"While various deep learning techniques for electroencephalogram (EEG)-based brainprint recognition have achieved considerable success, these models remain vulnerable to adversarial attacks. However, existing black-box attack methods suffer from an inherent trade-off between query efficiency and distortion level. To address this challenge and further investigate the security risks of brainprint recognition systems in real-world black-box scenarios, we propose a query-efficient, low-distortion black-box attack method that targets the high-frequency components of EEG signals. Our approach innovatively selects sparse sampling points to estimate more accurate gradient information and leverages historical gradients to guide the prioritization of important points, thereby accelerating the attack process. The perturbations are applied in the high-frequency domain of the EEG signal to enhance stealth and effectiveness. Extensive experiments under black-box settings demonstrate that our method achieves state-of-the-art performance across two datasets and four models. Compared to existing methods, our approach significantly improves attack success rates while reducing the number of queries and minimizing distortion to imperceptible levels, thus achieving a superior balance between query efficiency and perturbation stealth.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"2020-2024"},"PeriodicalIF":3.2000,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QELDBA: Query-Efficient and Low Distortion Black-Box Attack for Brainprint Recognition\",\"authors\":\"Jingsheng Qian;Hangjie Yi;Honggang Liu;Xuanyu Jin;Wanzeng Kong\",\"doi\":\"10.1109/LSP.2025.3563446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While various deep learning techniques for electroencephalogram (EEG)-based brainprint recognition have achieved considerable success, these models remain vulnerable to adversarial attacks. However, existing black-box attack methods suffer from an inherent trade-off between query efficiency and distortion level. To address this challenge and further investigate the security risks of brainprint recognition systems in real-world black-box scenarios, we propose a query-efficient, low-distortion black-box attack method that targets the high-frequency components of EEG signals. Our approach innovatively selects sparse sampling points to estimate more accurate gradient information and leverages historical gradients to guide the prioritization of important points, thereby accelerating the attack process. The perturbations are applied in the high-frequency domain of the EEG signal to enhance stealth and effectiveness. Extensive experiments under black-box settings demonstrate that our method achieves state-of-the-art performance across two datasets and four models. Compared to existing methods, our approach significantly improves attack success rates while reducing the number of queries and minimizing distortion to imperceptible levels, thus achieving a superior balance between query efficiency and perturbation stealth.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":\"32 \",\"pages\":\"2020-2024\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10974455/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10974455/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
QELDBA: Query-Efficient and Low Distortion Black-Box Attack for Brainprint Recognition
While various deep learning techniques for electroencephalogram (EEG)-based brainprint recognition have achieved considerable success, these models remain vulnerable to adversarial attacks. However, existing black-box attack methods suffer from an inherent trade-off between query efficiency and distortion level. To address this challenge and further investigate the security risks of brainprint recognition systems in real-world black-box scenarios, we propose a query-efficient, low-distortion black-box attack method that targets the high-frequency components of EEG signals. Our approach innovatively selects sparse sampling points to estimate more accurate gradient information and leverages historical gradients to guide the prioritization of important points, thereby accelerating the attack process. The perturbations are applied in the high-frequency domain of the EEG signal to enhance stealth and effectiveness. Extensive experiments under black-box settings demonstrate that our method achieves state-of-the-art performance across two datasets and four models. Compared to existing methods, our approach significantly improves attack success rates while reducing the number of queries and minimizing distortion to imperceptible levels, thus achieving a superior balance between query efficiency and perturbation stealth.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.