论文标题
部分可观测时空混沌系统的无模型预测
Deepfake Forensics Using Recurrent Neural Networks
论文作者
论文摘要
截至最近，基于AI的免费编程设备使在录音中进行真实的面部掉期变得简单，这些录音几乎没有任何控制迹象，即所谓的“ Deepfake”录音。实际上想象着将这些真实的伪造录音用来造成政治痛苦，勒索某人或基于伪装的压迫场合的情况。本文提出了一条短暂的正念管道，以自动识别深层记录。我们的框架利用卷积神经系统（CNN）删除轮廓级别的亮点。然后，这些亮点用于准备重复的神经网络工作（RNN），该神经网（RNN）弄清楚了如何表征视频是否已对控制进行表征。我们评估了我们的技术，以从不同的视频网站收集的深层录音的巨大安排。我们展示了我们的框架如何在利用基本设计的同时，在这项任务中取得了积极的成果。
As of late an AI based free programming device has made it simple to make authentic face swaps in recordings that leaves barely any hints of control, in what are known as "deepfake" recordings. Situations where these genuine istic counterfeit recordings are utilized to make political pain, extort somebody or phony fear based oppression occasions are effectively imagined. This paper proposes a transient mindful pipeline to automat-ically recognize deepfake recordings. Our framework utilizes a convolutional neural system (CNN) to remove outline level highlights. These highlights are then used to prepare a repetitive neural net-work (RNN) that figures out how to characterize if a video has been sub-ject to control or not. We assess our technique against a huge arrangement of deepfake recordings gathered from different video sites. We show how our framework can accomplish aggressive outcomes in this assignment while utilizing a basic design.
