Deep Fake Detection

Abstract

The rapid growth of generative artificial intelligence has brought about DeepFakes, which represent a significant threat to the authenticity of digital content. These synthetic media, mainly involving facial alterations in videos, pose serious ethical, security, and societal concerns. This paper details the creation of a DeepFake detection system utilizing machine learning techniques, designed to effectively identify manipulated facial videos. The proposed method involves preprocessing a collection of authentic and DeepFake videos, extracting individual frames, and training a convolutional neural network (CNN) to classify the footage into genuine or altered categories. The solution employs Python along with popular libraries such as TensorFlow, Keras, and OpenCV to build a comprehensive detection pipeline. Experimental results demonstrate the model's ability to accurately differentiate between real and fake content, contributing to the growing field of digital media forensics. This study emphasizes the critical role of automated detection tools in combating the malicious use of AI-generated media and maintaining content integrity across digital platforms.