Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Deepfake detection by utilizing electroencephalography (EEG) measured from the neural processing of a human participant who viewed and categorized Deepfake stimuli from the FaceForensics++ datset. These measurements serve as input features to a binary support vector classifier, trained to discriminate between real and manipulated facial images. We examine whether EEG data can inform Deepfake detection and also if it can provide a generalized representation capable of identifying Deepfakes beyond the training domain. Our preliminary results indicate that human neural processing signals can be successfully integrated into Deepfake detection frameworks and hint at the potential for a generalized neural representation of artifacts in computer generated faces. Moreover, our study provides next steps towards the understanding of how digital realism is embedded in the human cognitive system, possibly enabling the development of more realistic digital avatars in the future.

翻译：自深度伪造技术出现于数字媒体以来，迫切需要开发稳健可靠的检测机制。本研究探索了一种利用脑电图（EEG）进行深度伪造检测的新方法——通过测量人类被试在观看并分类FaceForensics++数据集中的深度伪造刺激时产生的神经处理信号，将这些测量值作为二元支持向量分类器的输入特征，以区分真实与篡改的人脸图像。我们不仅检验了脑电数据能否辅助深度伪造检测，还探究其是否具备超越训练域识别深度伪造的通用表征能力。初步结果表明，人类神经处理信号可成功整合至深度伪造检测框架中，并揭示了计算机生成人脸伪影可能存在的通用神经表征。此外，本研究为理解数字真实感如何嵌入人类认知系统提供了后续方向，有望推动未来更具真实感的数字虚拟化身开发。