VIDEO FORGERY IDENTIFICATION APPLICATION

VIDEO FORGERY IDENTIFICATION APPLICATION

Meenakshi, Siliveru Prasanna Lakshmi , Kaira Abhiram , Chithanuri Vamshi Krishna

Student (MGIT)

Abstract:

The rapid advancement of deepfake technology has raised significant concerns about the authenticity of visual content. Deep fake videos, which convincingly superimpose one person's likeness onto another's, can be used for both harmless entertainment and malicious misinformation.

Detecting these manipulated videos is crucial for maintaining trust and accuracy in media. In this study, we propose a novel approach for deep fake video detection using deep learning techniques, coupled with a user-friendly graphical user.

Our methodology involves training a deep neural network using a large dataset of both real and deep fake videos. These features are then fed in to a series of convolutional and recurrent layers to capture both spatial and temporal information.

Keywords: Superimpose, Misinformation, Neural Network, convolutional, spatial.