Recent advances in artificial intelligence make it progressively hard to distinguish between genuine and counterfeit media, especially images and videos. One recent development is the rise of deepfake videos, based on manipulating videos using advanced machine learning techniques. This involves replacing the face of an individual from a source video with the face of a second person, in the destination video. This idea is becoming progressively refined as deepfakes are getting progressively seamless and simpler to compute. Combined with the outreach and speed of social media, deepfakes could easily fool individuals when depicting someone saying things that never happened and thus could persuade people in believing fictional scenarios, creating distress, and spreading fake news. In this paper, we examine a technique for possible identification of deepfake videos. We use Euler video magnification which applies spatial decomposition and temporal filtering on video data to highlight and magnify hidden features like skin pulsation and subtle motions. Our approach uses features extracted from the Euler technique to train three models to classify counterfeit and unaltered videos and compare the results with existing techniques.

翻译：最近人工智能的进步使得人们越来越难以区分真实媒体和假冒媒体,特别是图像和视频。最近的一项发展是,在利用先进机器学习技术操纵视频的基础上,出现了深假视频。这涉及将一个人的脸从源视频中替换为目的地视频中的第二个人的脸。随着深假逐渐无缝和简单化的计算,这种想法正在逐渐得到完善。与社交媒体的推广和速度相结合,深假在描绘某人说从未发生过的事情时很容易愚弄个人,从而可以说服人们相信虚构的情景,制造痛苦,传播假消息。在本文中,我们研究了一种可能用来识别深假视频的技术。我们使用Euler视频放大技术,对视频数据进行空间分解和时间过滤,以突出和放大隐藏的特征,如皮肤脉冲和微妙动作。我们的方法利用从Euler技术中提取的特征来训练三个模型,对假冒和未变的视频进行分类,并将结果与现有技术进行比较。