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 1 - Deepfake Detection Website
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 3 - System Architecture
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 4 - Implementation & Results
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.
Slide 5 - Conclusion & Future Scope

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