EEG-based brainprint recognition with deep learning models has garnered much attention in biometric identification. Yet, studies have indicated vulnerability to adversarial attacks in deep learning models with EEG inputs. In this paper, we introduce a novel adversarial attack method that jointly attacks time-domain and frequency-domain EEG signals by employing wavelet transform. Different from most existing methods which only target time-domain EEG signals, our method not only takes advantage of the time-domain attack's potent adversarial strength but also benefits from the imperceptibility inherent in frequency-domain attack, achieving a better balance between attack performance and imperceptibility. Extensive experiments are conducted in both white- and grey-box scenarios and the results demonstrate that our attack method achieves state-of-the-art attack performance on three datasets and three deep-learning models. In the meanwhile, the perturbations in the signals attacked by our method are barely perceptible to the human visual system.

翻译：基于脑电图（EEG）并利用深度学习模型进行脑纹识别在生物特征识别领域已引起广泛关注。然而，研究表明，接受EEG输入的深度学习模型容易受到对抗攻击。本文提出了一种新颖的对抗攻击方法，该方法通过运用小波变换，联合攻击时域和频域的EEG信号。与大多数仅针对时域EEG信号的现有方法不同，我们的方法不仅利用了时域攻击强大的对抗性优势，还受益于频域攻击固有的不可感知性，从而在攻击性能和不可感知性之间实现了更好的平衡。在白盒和灰盒场景下进行了大量实验，结果表明，我们的攻击方法在三个数据集和三个深度学习模型上均达到了最先进的攻击性能。同时，经我们方法攻击后的信号中的扰动对人眼视觉系统而言几乎无法察觉。