01 Synopsis
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- Up to 30 pages
- Only 1 diagram included
- Problem statement & objectives
- Ready for college submission
M.Tech Project Report
M.Tech Deepfake Detection Final Year Machine Learning Project Report
Complete M.Tech final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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02 Pre Defined Project Report
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- Up to 70 pages
- ER Diagram & DFD Diagrams
- Up to 8 diagrams included
- Instant download
03 Customized Report
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- Tailored to your college format
- Personalized content
- Faculty-aligned structure
- Delivery within 24-48 hours
04 Originality Reviewed
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- AI detection reviewed
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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—
Abstract
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—
Table of Content
-
01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
Deepfake Detection Using Machine Learning is a Python Flask based final year project developed for detecting deepfake images and videos using machine learning. This major project includes a public landing page, user dashboard, and admin dashboard. Users can register, log in, upload images, upload videos, run deepfake detection, view real/fake predictions, check confidence scores, view frame-wise video analysis, download PDF or TXT reports, manage detection history, update profile details, change password, and securely log out. Admins can monitor total users, total detections, real media count, deepfake count, failed detections, recent users, all detection records, and user accounts. The detection pipeline uses OpenCV face detection, feature extraction, Xception + LSTM machine learning model prediction, and local processing without third-party APIs. This deepfake detection source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, Flask, TensorFlow, Keras, OpenCV, and machine learning.
Login Credentials
Admin Account
- Email:
[email protected] - Username:
admin - Password:
admin123 - Dashboard:
/admin/
Sample User Accounts
All sample users use password:
password123
Sample users:
- Username:
johnsmith— Email:[email protected] - Username:
sarahj— Email:[email protected] - Username:
mikebrown— Email:[email protected] - Username:
emilyd— Email:[email protected] - Username:
davidw— Email:[email protected] - Username:
lisaanderson— Email:[email protected] - Username:
jamest— Email:[email protected] - Username:
mariag— Email:[email protected]
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- Admin login in this final year project
- Admin dashboard
- View total users
- View total detections
- View real media count
- View deepfake count
- View failed detections
- View latest detections across all users
- View latest registered users
- View ML model type
- View local processing status
- View dataset information
- Manage users
- View paginated user list
- View user ID, name, email, username, mobile, and join date
- Delete user account
- Delete user detection records and uploaded files
- Manage all detections
- View paginated detection list
- View detection ID, user, file, type, result, confidence, status, and date
- Delete detection record
- Delete associated uploaded file
- Admin-only route protection
- Admin dashboard sidebar navigation
- User registration in this final year project
- User login using email or username
- Remember Me login option
- Secure logout
- User dashboard with total detections
- View real media count
- View deepfake count
- View recent detections
- Quick action links for upload, history, and profile
- Upload image for deepfake detection
- Upload video for deepfake detection
- Drag-and-drop file upload interface
- File type validation
- File size validation up to 100 MB
- Image support: JPG, JPEG, PNG
- Video support: MP4, AVI, MOV
- Run deepfake detection pipeline
- View detection result
- View REAL or DEEPFAKE verdict
- View real confidence percentage
- View fake confidence percentage
- View file type and file size
- View faces detected count
- View detection status and date
- View video result summary
- View total frames analyzed
- View real frame count
- View fake frame count
- View frame-by-frame prediction table
- View detailed report
- Download report as PDF
- Download report as TXT
- View detection history
- Paginated detection history table
- View previous upload result
- Delete old detection record
- Update profile name and mobile number
- Change password with old password verification
- View clear error messages for invalid login, unsupported file, large file, missing face, and failed prediction
- Complete deepfake detection using machine learning source code
- Suitable for final year project, major project, and minor project
- Project report content can be prepared from included modules
- Public Landing Page, User Dashboard, and Admin Dashboard included
- Image and video detection support
- Kaggle-style deepfake dataset model support
- Xception + LSTM architecture support
- TensorFlow and Keras based ML model
- OpenCV based face detection
- SQLite database storage
- Flask-SQLAlchemy ORM
- Flask-Login authentication
- Flask-WTF forms and validation
- Werkzeug password hashing
- Secure session management with 12-hour lifetime
- Secure file handling
- Per-user upload storage
- Detection history with pagination
- PDF and TXT report download
- Admin user and detection monitoring
- Seed data included
- Sample detection records included
- Model weights folder included
- Uploads folder auto-created
- Reports folder auto-created
- Useful for viva, source code review, project report writing, and project demonstration
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Open project folder:
cd "Deepfake Detection Using Machine Learning" -
Create virtual environment:
python -m venv venv -
Activate virtual environment on Windows:
venv\Scripts\activate -
Activate virtual environment on Linux/macOS:
source venv/bin/activate -
Install dependencies:
pip install -r requirements.txt -
Place trained ML model weights file:
ml_models/deepfake_model.weights.h5 - If model weights are missing, the app runs in heuristic fallback mode.
-
Seed the database:
python seed.py -
Start the application:
python app.py - Open project in browser:
http://localhost:5000
Most Important - Download the Pre Trained Model File from here.
README Included
Setup Support Available
Admin Account
- Email:
[email protected] - Username:
admin - Password:
admin123 - Dashboard:
/admin/
Sample User Accounts
All sample users use password:
password123
Sample users:
- Username:
johnsmith— Email:[email protected] - Username:
sarahj— Email:[email protected] - Username:
mikebrown— Email:[email protected] - Username:
emilyd— Email:[email protected] - Username:
davidw— Email:[email protected] - Username:
lisaanderson— Email:[email protected] - Username:
jamest— Email:[email protected] - Username:
mariag— Email:[email protected]