![]() Fake images or videos generated by facial image manipulation are rapidly spread and can be utilized in news or social network services. Various techniques for manipulating facial images, such as morphing 5, 6, swapping 7, 8, and retouching 9, 10, have been proposed. Facial image manipulation is a type of digital image manipulation 4 and refers to a technique of synthesizing or replacing a face within an image with another face. Similar content being viewed by othersÄue to the recent development of artificial intelligence technology, the performance of facial image manipulation technology, widely known as Deepfake, has greatly improved 1, 2, 3. In particular, by applying the expression swap model to widely used online meeting platforms such as Zoom, Google Meet, and Microsoft Teams, we demonstrate its feasibility for real-time online classes. Finally, we devise an architecture for applying the expression swap model to the online video conferencing application in real-time. In contrast, GANimation has the advantages of representing facial expression changes compared to the first order model. However, their performances are significantly degraded in Scenarios 2 and 3, where the face occupies less portion of the image the first order model causes relatively less loss of image quality than GANimation in the result of the quantitative evaluation. Specifically, both models show acceptable results in Scenario 1, where the face occupies a large portion of the image. Through the quantitative and qualitative evaluation, we observe distinguishing properties of the used two models. We implement these models in the framework and evaluate their performance for the defined scenarios. To this end, we select two models that satisfy the conditions required by the framework: (1) first order model and (2) GANimation. Considering the manipulation on the online class environments, the framework receives a single source image and a target video and generates the video that manipulates a face of the target video to that in the source image. ![]() For this, we define three kinds of scenarios according to the portion of the face in the entire image considering actual online class situations: (1) attendance check (Scenario 1), (2) presentation (Scenario 2), and (3) examination (Scenario 3). In this study, we propose an evaluation framework of the expression swap models targeting the real-time online class environments. Out of them, we focus on expression swap because it effectively manipulates only the expression of the face in the images or videos without creating or replacing the entire face, having advantages for the real-time application. The techniques for facial image manipulation are classified into four categories: (1) entire face synthesis, (2) identity swap, (3) attribute manipulation, and (4) expression swap. Facial image manipulation synthesizes or replaces a region of the face in an image with that of another face. Recent advances in artificial intelligence technology have significantly improved facial image manipulation, which is known as Deepfake.
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