The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. In this study, we use deep transfer learning for face swapping detection, showing true positive rates greater than 96% with very few false alarms. Distinguished from existing methods that only provide detection accuracy, we also provide uncertainty for each prediction, which is critical for trust in the deployment of such detection systems. Moreover, we provide a comparison to human subjects. To capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, we infer a consensus ranking from the image perceived as most real to the image perceived as most fake. Overall, the results show the effectiveness of our method. As part of this study, we create a novel dataset that is, to the best of our knowledge, the largest swapped face dataset created using still images. This dataset will be available for academic research use per request. Our goal of this study is to inspire more research in the field of image forensics through the creation of a dataset and initial analysis.

中文翻译：

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使用深度学习和主观评估进行面部表情交换

深度学习在成像应用中的巨大成功导致了许多有益的进步。不幸的是，这种成功也成为恶意使用的催化剂，例如未经同意就对各方进行照片般逼真的面部交换。在这项研究中，我们使用深度转移学习进行人脸交换检测，显示出真正的阳性率大于96％，很少出现错误警报。与仅提供检测准确性的现有方法不同，我们还为每个预测提供不确定性，这对于信任此类检测系统的部署至关重要。此外，我们提供了与人类受试者的比较。为了捕获人类的识别性能，我们建立了一个网站来收集来自人类对象的图像的成对比较。根据这些比较，我们从感知到最真实的图像到感知到最伪造的图像推断出共识等级。总体而言，结果表明了我们方法的有效性。作为这项研究的一部分，我们创建了一个新颖的数据集，据我们所知，它是使用静态图像创建的最大的互换面部数据集。根据请求，该数据集可用于学术研究。这项研究的目的是通过创建数据集和初步分析来激发更多关于图像取证领域的研究。