Deepfake Caricatures：放大对伪影的注意力可提高人类和机器的deepfake检测能力
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines
AI总结：
本文提出一种放大deepfake视频伪影的框架，通过半监督Artifact Attention模块生成视觉指示器Deepfake Caricatures，显著提升人类检测能力，并改进检测模型的准确性和鲁棒性。
摘要：
Deepfakes可能助长网络虚假信息。随着deepfakes越来越难以用肉眼识别，人类用户越来越依赖deepfake检测模型来帮助他们判断视频是真实的还是伪造的。目前，模型会对视频的真实性给出预测，但并未集成向人类用户发出警报的方法。我们引入了一个框架，用于放大deepfake视频中的伪影，使其更容易被人类检测到。我们提出了一个新颖的半监督Artifact Attention模块，该模块基于人类响应进行训练，以创建突出显示视频伪影的注意力图，并将其放大以创建一种我们称之为“Deepfake Caricatures”的新颖视觉指示器。在一项用户研究中，我们证明Caricatures在不同的视频呈现时间和用户参与度水平下极大地提高了人类的检测能力。我们还引入了一个结合Artifact Attention模块的deepfake检测模型，以提高其准确性和鲁棒性。总体而言，我们展示了以人为中心设计deepfake缓解方法的成功。
英文摘要：
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.
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。
