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.

通过利用从人类参与者的神经处理中测量得到的脑电图（EEG）从 FaceForensics++ 数据集的 Deepfake 刺激中检测深度伪造是这项研究的目标。初步结果表明，人类神经处理信号成功地整合到了 Deepfake 检测框架中，并且暗示了一种能够识别超越训练领域的 Deepfake 的广义神经表征的潜力。此外，我们的研究为理解数字真实主义如何嵌入人类认知系统提供了下一步的步骤，可能推动未来更逼真的数字化身的发展。