论文标题
检测DeepFake视频：对三种技术的分析
Detecting Deepfake Videos: An Analysis of Three Techniques
论文作者
论文摘要
产生媒体的算法的最新进展对隐私，安全和大众传播产生了危险的影响。解决这个问题的努力已经以竞争和研究资金的形式增加，以检测深层效果。本文介绍了三种技术和算法：卷积LSTM，眼睛眨眼检测和灰度直方图，同时参与了DeepFake检测挑战。我们评估了当前有关DeepFake视频的知识，更严重的操纵媒体以及以前使用的方法，并在其他方法上发现了相关性。我们讨论了开发的每种方法的含义，并提供了进一步的步骤来改善给定的发现。
Recent advances in deepfake generating algorithms that produce manipulated media have had dangerous implications in privacy, security and mass communication. Efforts to combat this issue have risen in the form of competitions and funding for research to detect deepfakes. This paper presents three techniques and algorithms: convolutional LSTM, eye blink detection and grayscale histograms-pursued while participating in the Deepfake Detection Challenge. We assessed the current knowledge about deepfake videos, a more severe version of manipulated media, and previous methods used, and found relevance in the grayscale histogram technique over others. We discussed the implications of each method developed and provided further steps to improve the given findings.
