Syncnet Model with VidTIMIT Dataset

Syncnet Model with VidTIMIT Dataset

Syncnet Model with VidTIMIT Dataset. Predicting the video as real or fake.

Introduction

There is a lot of advancement in video manipulation techniques. And it’s a lot easier to create tampered videos, which can fool human eyes. Such content leads to fake news or misinformation. And which can affect the people and the countries.

So in this project, I tried to detect whether the video is tampered or not, or you can say, real or fake? I referred to this research paper throughout my project. They focused on determining the audio-video synchronization between mouth motion and speech in the video. They used audio-video synchronization for TV broadcasting. But here I used the VidTIMIT dataset. It’s really a nice research paper, they developed a language-independent and speaker-independent solution to the lip-sync problem, without labeled data.

There are more latest research papers out there for the same problem. Like, this and this. And their results are also good. But I chose to apply the syncnet model as it’s quite easy, simple model structure and we have pretrained weights available for the model. So let's see more about the syncnet model and its architecture.

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