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 persons 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.
翻译:近年来,社交媒体已发展成为许多在线用户的主要信息来源。 这导致通过深假传播错误信息。 深假是视频或图像,用另一个计算机产生的面孔取代一个人的面孔, 往往是社会上一个更能识别的人。 随着科技的最新进步, 一个技术经验少的人可以生成这些视频。 这使他们能够模仿社会中的力量人物, 如总统或名人, 从而产生传播错误信息和其他错误使用深假的潜在危险。 为了应对这一威胁, 研究人员开发了用来探测深假威胁的模型。 本研究考察了各种使用深学习算法来对付这一迫在眉睫的威胁的深假发现模型。 本调查的重点是全面概述深假检测模型的现状和许多研究人员为解决这一问题采取的独特方法。 本文将全面讨论未来工作的好处、限制和建议。