Deep Fake detection Using Deep Learning for Images, Videos & Audios

Abstract

Deepfake detection has emerged as a significant challenge in the field of artificial intelligence due to the rapid advancement of generative models such as Generative Adversarial Networks (GANs). These techniques enable the creation of highly realistic manipulated media, including images, videos, and audio, which can be misused for misinformation, identity theft, and cybercrime.

This project presents a deep learning-based system for detecting deepfake content using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The system is designed to process multi-modal inputs such as images, videos, and audio, and performs both spatial and temporal analysis to identify inconsistencies in manipulated media.

The system provides a user-friendly interface for uploading media files, analyzing authenticity, and generating results with high accuracy. The proposed solution improves detection reliability, reduces manual effort, and enhances digital media security. The paper concludes with experimental evaluation and discusses future improvements such as real-time detection and enhanced robustness against advanced deepfake techniques.