Deepfake Forensics Using Recurrent Neural Networks
This work addresses the threat of deepfake videos used for political manipulation or extortion, but it is incremental as it builds on existing neural network methods.
The paper tackles the problem of detecting deepfake videos by proposing a temporal-aware pipeline that uses a CNN for frame-level feature extraction and an RNN for classification, achieving competitive results on a large dataset of deepfake videos.
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.