利用双重神经网络操作痕迹的个性化深度伪造检测
Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations
AI总结：
针对特定公众人物的深度伪造检测问题，提出利用神经网络幂等性进行双重操作以优化检测器，使AUC提升至0.94，并发布了包含45位公众人物约3.2万张图像的数据集。
摘要：
在当今的数字环境中，记者迫切需要工具来验证描绘特定公众人物的面部图像和视频的真实性，然后再将其纳入新闻报道。当图像与特定且可识别的个人相关联时，现有的深度伪造检测器并未针对这一检测任务进行优化。本研究专注于个别公众人物面部图像的深度伪造检测。鉴于我们的理论驱动仿真所揭示的优势，我们提出将所提出的检测器以已识别个人的身份为条件进行训练。虽然文献中的大多数检测器依赖于深度伪造面部图像中存在的可感知或不可感知的伪影，但我们证明了通过利用神经网络的幂等性属性可以提高检测性能。在我们的方法中，训练过程涉及双重神经网络操作，即我们将真实图像通过深度伪造模拟网络两次。实验结果表明，所提出的方法将曲线下面积（AUC）从0.92提高到0.94，并将其标准差降低了17%。为了满足评估个别公众人物检测性能的需求，我们策划并公开发布了一个包含约32k张图像、以45位公众人物为特征的数据集，因为现有的深度伪造数据集不符合这一标准。
英文摘要：
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 an 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%. To address the need for evaluating detection performance for individual public figures, we curated and publicly released a dataset of ~32k images featuring 45 public figures, as existing deepfake datasets do not meet this criterion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
