视频会议的深度造假与安全

Ahmet Semih Uçan, Fatih Mustafa Buçak, Mehmet Ali Han Tutuk, Halis İbrahim Aydin, Ertuğrul Semiz, Şerif Bahtiyar
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引用次数: 3

摘要

深度学习被广泛用于在互联网上创建人工内容。同样,它也用于检测虚假内容。创建并集成深度学习算法的假框架被称为deepfake。最近,恶意用户倾向于利用deepfake操纵正版内容进行各种攻击。自新冠肺炎疫情爆发以来,视频会议应用一直是恶意用户的主要目标,他们利用深度伪造模型在在线视频会议中创建虚假虚拟身份。我们提出了一种轻量级的深度假检测模型,可以与视频会议应用程序集成以检测假脸。实验分析表明,该模型对视频会议中的假图像检测具有可接受的精度。
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Deepfake and Security of Video Conferences
Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep learning algorithms are known as deepfake. Recently, malicious users tend to use deepfake to manipulate genuine contents to carry out variety of attacks. Video conferencing applications has been a significant target of the malicious users since the beginning of Covid-19 pandemic who use deepfake models to create fake virtual identities in online video conferences. We propose a lightweight deepfake detection model that may be integrated with video conference applications to detect fake faces. Experimental analyses show that the proposed model provides acceptable accuracy to detect fake images on video conferences.
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