使用深度学习检测深度造假

Jacob Mallet, Rushit Dave, Naeem Seliya, Mounika Vanamala
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引用次数: 9

摘要

近年来,社交媒体已经发展成为许多在线用户的主要信息来源。这导致了虚假信息通过深度造假的传播。深度伪造是指用电脑生成的另一张人脸代替一个人的脸的视频或图像,通常是社会上更容易识别的人。随着最近技术的进步,一个没有技术经验的人也可以制作这些视频。这使他们能够模仿社会上的权力人物,如总统或名人,从而产生传播错误信息和其他恶意使用深度伪造的潜在危险。为了对抗这种在线威胁,研究人员开发了旨在检测深度伪造的模型。这项研究着眼于各种深度伪造检测模型,这些模型使用深度学习算法来对抗这种迫在眉睫的威胁。本调查的重点是全面概述深度伪造检测模型的现状,以及许多研究人员解决这一问题的独特方法。本文将全面讨论其优点、局限性和对未来工作的建议。
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Using Deep Learning to Detecting Deepfakes
In the recent years, social media has grown to become a major source of information for many online users. This has given rise to the spread of misinformation through deepfakes. Deepfakes are videos or images that replace one person's face with another computer-generated face, often a more recognizable person in society. With the recent advances in technology, a person with little technological experience can generate these videos. This enables them to mimic a power figure in society, such as a president or celebrity, creating the potential danger of spreading misinformation and other nefarious uses of deepfakes. To combat this online threat, researchers have developed models that are designed to detect deepfakes. This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat. This survey focuses on providing a comprehensive overview of the current state of deepfake detection models and the unique approaches many researchers take to solving this problem. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.
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