Visual Realism Assessment for Face-swap Videos
Xianyun Sun, Beibei Dong, Caiyong Wang, Bo Peng, Jing Dong

TL;DR
This paper introduces a benchmark for evaluating visual realism assessment models for face-swap videos, highlighting the effectiveness of deep-learning features and providing a comprehensive comparison of different approaches.
Contribution
It establishes a benchmark dataset and evaluation framework for assessing human-perceived realism of face-swap videos, exploring traditional and deep-learning based VRA models.
Findings
Deep-learning features improve VRA accuracy
Traditional features are still competitive in some scenarios
Existing deepfake detection features are useful for VRA
Abstract
Deep-learning based face-swap videos, also known as deep fakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also important as a quality assessment metric to compare different face-swap methods. In this paper, we make a small step towards this new VRA direction by building a benchmark for evaluating the effectiveness of different automatic VRA models, which range from using traditional hand-crafted features to different kinds of deep-learning features. The evaluations are…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection · Misinformation and Its Impacts
