论文标题
深层漫画：放大对工件的关注会增加人类和机器的深层检测
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines
论文作者
论文摘要
Deepfakes可以推动在线错误信息。随着肉眼越来越难以识别，人类用户变得越来越依赖DeepFake检测模型，以帮助他们确定视频是真实的还是假的。当前，模型对视频的真实性产生了预测，但不整合提醒人用户的方法。我们介绍了一个框架，用于在Deepfake视频中放大工件，以使其更容易被人们检测到。我们提出了一个新颖的，半监督的伪影注意模块，该模块对人类的反应进行了训练，以创建注意力图，以突出视频文物，并放大它们，以创建一个新颖的视觉指示器，我们称为“深胶漫画”。在一项用户研究中，我们证明漫画大大增加了在视频演示时间和用户参与度之间的人类检测。我们还引入了一个深层检测模型，该模型结合了工件注意模块以提高其准确性和鲁棒性。总体而言，我们证明了以人为中心的方法来设计缓解深泡方法的成功。
Deepfakes can fuel online misinformation. As deepfakes get harder to recognize with the naked eye, human users become more reliant on deepfake detection models to help them decide whether a video is real or fake. Currently, models yield a prediction for a video's authenticity, but do not integrate a method for alerting a human user. We introduce a framework for amplifying artifacts in deepfake videos to make them more detectable by people. We propose a novel, semi-supervised Artifact Attention module, which is trained on human responses to create attention maps that highlight video artifacts, and magnify them to create a novel visual indicator we call "Deepfake Caricatures". In a user study, we demonstrate that Caricatures greatly increase human detection, across video presentation times and user engagement levels. We also introduce a deepfake detection model that incorporates the Artifact Attention module to increase its accuracy and robustness. Overall, we demonstrate the success of a human-centered approach to designing deepfake mitigation methods.
