In today's digital landscape, journalists urgently require tools to verify the authenticity of facial images and videos depicting specific public figures before incorporating them into news stories. Existing deepfake detectors are not optimized for this detection task when an image is associated with a specific and identifiable individual. This study focuses on the deepfake detection of facial images of individual public figures. We propose to condition the proposed detector on the identity of the identified individual given the advantages revealed by our theory-driven simulations. While most detectors in the literature rely on perceptible or imperceptible artifacts present in deepfake facial images, we demonstrate that the detection performance can be improved by exploiting the idempotency property of neural networks. In our approach, the training process involves double neural-network operations where we pass an authentic image through a deepfake simulating network twice. Experimental results show that the proposed method improves the area under the curve (AUC) from 0.92 to 0.94 and reduces its standard deviation by 17\%. For evaluating the detection performance of individual public figures, a facial image dataset with individuals' names is required, a criterion not met by the current deepfake datasets. To address this, we curated a dataset comprising 32k images featuring 45 public figures, which we intend to release to the public after the paper is published.

翻译：在当今的数字环境中，记者在将特定公众人物的面部图像和视频纳入新闻报道之前，急需验证其真实性的工具。现有的深度伪造检测器在图像关联特定可识别个体时，并未针对此类检测任务进行优化。本研究聚焦于个体公众人物面部图像的深度伪造检测。鉴于理论驱动模拟揭示的优势，我们提出根据已识别个体的身份对检测器进行条件化处理。尽管文献中的多数检测器依赖深度伪造面部图像中可感知或不可感知的伪影，但我们证明，利用神经网络的等幂性可提升检测性能。在我们的方法中，训练过程涉及双神经网络操作，即通过深度伪造模拟网络两次传递真实图像。实验结果表明，所提方法将曲线下面积（AUC）从0.92提升至0.94，并将其标准差降低了17%。为评估个体公众人物的检测性能，需要包含个体姓名的面部图像数据集，而当前深度伪造数据集未满足这一标准。为此，我们整理了一个包含45位公众人物32000张图像的数据集，并计划在论文发表后向公众开放。