Video forgery detection

Now-a-days video is most important approach to get information and collect evidence of any crime. These videos can be easily edited and tempered using different video editing tools like Veges, Openshot, Blender and Adobe Premier etc. These videos are shared on social media to misguide and for differ

Project Title

Video forgery detection

Project Area of Specialization

Artificial Intelligence

Project Summary

Now-a-days video is most important approach to get information and collect evidence of any crime. These videos can be easily edited and tempered using different video editing tools like Veges, Openshot, Blender and Adobe Premier etc. These videos are shared on social media to misguide and for different malicious purposes. Videos are forged using different ways but we focus on temporal or frame based tempering in this project. The tempering frames are taken from other videos and paste them in the original video or frames are also deleted from the video. The aim of the video forgery detection is to find forged frames and also where the frames are forged i.e. inserted and deleted.  Now-a-days video is most important approach to get information and collect evidence of any crime. These videos can be easily edited and tempered using different video editing tools like Veges, Openshot, Blender and Adobe Premier etc. These videos are shared on social media to misguide and for different malicious purposes. Videos are forged using different ways but we focus on temporal or frame based tempering in this project. The tempering frames are taken from other videos and paste them in the original video or frames are also deleted from the video. The aim of the video forgery detection is to find forged frames and also where the frames are forged i.e. inserted and deleted. 

Project Objectives

Video forgery detection detects the forged videos. The main objective of the project is to detect frames that are tempered and insertion and deletion in the original video. And also try to find out the region and location where the frames are inserted and deleted in the video.

Project Implementation Method

The development of robust representation of tempering is done by using deep learning approaches and we also use python to implement this project. In the graphical representation(Figure (1.1)) of method implementation, first we give input which is video then explain the features of video and train the modal and then modal give us result which video is original and fake.

Benefits of the Project

Many fields use videos as evidence and for information purpose. These videos can be edited using different tools. But all fields require authentication of videos. This software provide authentication of video to control the crimes and false propaganda on social media.

Technical Details of Final Deliverable

The final deliverable of the software is, give the video as input and then software find the forged frames of the video and their location as output to find original and duplicate video. The main purpose is to differ original and fake video.

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Security

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Quality Education

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com