基于查询效率和低失真的脑印识别黑盒攻击

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jingsheng Qian;Hangjie Yi;Honggang Liu;Xuanyu Jin;Wanzeng Kong
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引用次数: 0

摘要

尽管基于脑电图(EEG)的脑印识别的各种深度学习技术已经取得了相当大的成功,但这些模型仍然容易受到对抗性攻击。然而,现有的黑盒攻击方法在查询效率和失真程度之间存在固有的权衡。为了解决这一挑战并进一步研究现实世界黑箱场景下脑印识别系统的安全风险,我们提出了一种针对脑电图信号高频成分的查询高效、低失真黑箱攻击方法。我们的方法创新地选择稀疏采样点来估计更准确的梯度信息,并利用历史梯度来指导重要点的优先级,从而加快攻击过程。在脑电图信号的高频域施加扰动,增强了信号的隐蔽性和有效性。在黑盒设置下的大量实验表明,我们的方法在两个数据集和四个模型上实现了最先进的性能。与现有方法相比,我们的方法显著提高了攻击成功率,同时减少了查询数量,并将失真降至难以察觉的程度,从而在查询效率和微扰隐身之间实现了卓越的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
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.
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