Deepfake Detection Website

Generated from prompt:

Create a 5-slide PowerPoint presentation titled 'Deepfake Detection Website' based on a B.Tech project report. The slides should include: Slide 1: Introduction – Project title, team members (Aditya Harsh, Aradhy Tripathi, Shreyash Shaurya Srivastav), supervisor (Er. Paritosh Tripathi), institution (IET, Dr. Rammanohar Lohia Avadh University, Ayodhya), and overall goal of developing a deepfake detection website. Slide 2: Problem Statement & Objectives – Explain the growing issue of deepfakes, lack of real-time detection, and project objectives (MesoNet model, React.js frontend, Flask backend, cloud deployment). Slide 3: System Architecture – Describe components (frontend, backend, model, hosting) and show a high-level architecture flow. Slide 4: Implementation & Results – Steps of development (data preprocessing, model training, web integration, deployment) and performance metrics (accuracy 92.4%, precision 91.7%, recall 93.1%, F1-score 92.4%). Slide 5: Conclusion & Future Scope – Summarize outcomes and suggest future enhancements (video detection, mobile app, blockchain integration, media verification tools).

A B.Tech project developing a real-time deepfake detection website using MesoNet model, React.js frontend, Flask backend, and cloud deployment. Achieves 92.4% accuracy with plans for video detection a

January 16, 20265 slides

Slide 1 of 5

Slide 1 - Deepfake Detection Website

This title slide introduces a "Deepfake Detection Website" focused on a real-time deepfake detection system. It credits Team Aditya Harsh, Aradhy Tripathi, and Shreyash Shaurya Srivastav, under supervisor Er. Paritosh Tripathi at IET, Dr. Rammanohar Lohia Avadh University, Ayodhya.

Introduction

Real-Time Deepfake Detection System by Team Aditya Harsh, Aradhy Tripathi, Shreyash Shaurya Srivastav Supervisor: Er. Paritosh Tripathi IET, Dr. Rammanohar Lohia Avadh University, Ayodhya

Source: B.Tech Project Presentation

Speaker Notes

Introduce the project team, supervisor, institution, and goal during presentation.

Slide 2 of 5

Slide 2 - Problem Statement & Objectives

The slide addresses the growing threat of deepfakes driving misinformation and the lack of accessible real-time detection tools. It outlines objectives to develop a MesoNet deep learning model, build a React.js frontend and Flask backend, and deploy on the cloud for scalability.

Problem Statement & Objectives

Growing threat of deepfakes fueling misinformation campaigns
Lack of accessible real-time detection tools available
Develop MesoNet deep learning model for detection
Build React.js frontend for user interface
Implement Flask backend for API services
Deploy on cloud for scalability and performance

Source: Deepfake Detection Website

Slide 2 - Problem Statement & Objectives

Slide 3 of 5

Slide 3 - System Architecture

The slide outlines the system architecture as a workflow with phases including User Upload (Frontend: React.js), Processing (Backend API: Flask Backend), Inference (DL Model: MesoNet Model), Result Delivery (Output: Detection Result), and Deployment (Hosting: AWS/Heroku). It uses a tabular format with columns for Phase, Component, and Technology to detail each step.

System Architecture

Source: High-level flow: User Upload (React.js Frontend) → Video/Image Processing (Flask Backend) → MesoNet Model Inference → Detection Result → Cloud Hosting (AWS/Heroku)

Speaker Notes

The system follows a streamlined architecture with React.js frontend for user interaction, Flask backend for processing, MesoNet deep learning model for inference, and cloud deployment for scalability.

Slide 4 of 5

Slide 4 - Implementation & Results

The slide "Implementation & Results" showcases strong model performance with 92.4% accuracy and a balanced 92.4% F1-score. It also highlights 91.7% precision for low false positives and 93.1% recall for effective true positive detection.

Implementation & Results

92.4%: Model Accuracy

High detection performance

91.7%: Precision Score

Low false positives achieved

93.1%: Recall Score

Effective true positive capture

92.4%: F1-Score

Balanced model performance Source: B.Tech Project Report

Slide 5 of 5

Slide 5 - Conclusion & Future Scope

The slide summarizes a real-time deepfake detection website achieving 92.4% accuracy with the MesoNet model. It outlines future enhancements like video detection, mobile app, blockchain verification, and media tool integration, ending with a "Mission Accomplished!" message and a call to explore deepfake defenses.

Conclusion & Future Scope

**Project Summary ✅ Real-time deepfake detection website ✅ 92.4% accuracy using MesoNet model

Future Enhancements

Video deepfake detection
Mobile app development
Blockchain for verification
Integration with media tools

Closing Message: Mission Accomplished! Call-to-Action: Explore deepfake defense innovations today!**

Pioneering Trust in the Digital Age

Source: Deepfake Detection Website - B.Tech Project

Speaker Notes

Summarize the project's success and outline exciting future directions to engage the audience.

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