The technology to create realistic fake videos using AI is becoming increasingly sophisticated, making it difficult, if not impossible, to determine whether audio, images, or videos are real. Can humans or machines tell if a video is authentic, AI-generated, or altered? Has technology gotten to the point where there is no foolproof way to identify AI-altered videos? Manipulated videos are not a new issue; it is important to note that they can be created without AI. The advancement of AI, specifically deep neural networks and generative adversarial networks, has created sophisticated tools for realistic fake videos.

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.