Deepfake Detection Using Deep Learning (CNN) This project implements a Convolutional Neural Network (CNN) for detecting deepfake images and videos. With the rise of deepfakes in various domains, the model aims to identify manipulated content by analyzing visual inconsistencies and patterns.

Table of Contents Project Overview Installation Dataset Model Architecture Deepfakes are artificially generated media, often used maliciously to alter videos or images. This project uses a CNN-based approach to detect deepfakes by analyzing the visual artifacts and patterns that distinguish real from fake content. By training the model on a dataset of real and fake images, the system can detect deepfake media with high accuracy.

Installation- To set up and run the project, follow these steps:

Clone the repository: git clone https://github.com/rishabhGit24/Deepfake_Deeplearning_CNN.git cd Deepfake_Deeplearning_CNN

Model Architecture: This project uses a Convolutional Neural Network (CNN) for feature extraction and classification. The CNN architecture is designed to identify the subtle patterns present in deepfakes, such as inconsistencies in lighting, facial artifacts, and pixel manipulation.