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Deepfake Detection Final Year Project with Source Code

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Complete final-year project source code with frontend, backend, database, and setup guide. Instant download after secure payment.

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  • Complete project source files
  • Database script included
  • How-to-run guide
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Project Overview

Description, tech stack, and what is included

Full source Frontend + backend
Database .sql file
Setup guide README included

Deepfake Detection Final Year Project is a Python Flask based final year project developed for detecting fake videos and images using frequency-domain analysis and transformer-based machine learning. This major project includes a public landing page, user panel, and admin panel. Users can register, log in, upload videos, upload images, submit media for deepfake detection, view real/fake classification results, check confidence scores, review frame-level analysis, analyze frequency patterns, download PDF detection reports, manage profile, change password, submit feedback, and contact support. Admins can manage users, uploaded videos, detection requests, detection results, video categories, dataset records, ML settings, model training, system logs, login history, upload history, detection history, feedback, contact queries, daily reports, and monthly reports. 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, PyTorch, computer vision, and machine learning.

Technical snapshot

Project
Deepfake Detection Final Year Project with Source Code
Stack
MACHINE-LEARNING
Includes
Code, DB, README
License
Academic submission
Secure CCAvenue payment · Instant download · Need help? WhatsApp us

Ready to download?Pay once · Use for submission & viva

Admin Features

Modules and controls available to administrators

  • Admin login and logout in this final year project
  • Admin dashboard with system statistics
  • View recent detections
  • View system health
  • View recent users
  • View activity logs
  • Admin profile management
  • Change admin password
  • User management
  • View all users
  • Search users
  • Activate/deactivate user accounts
  • Delete user accounts
  • View full user details
  • View user detection history
  • View user uploaded videos
  • Video category management
  • Add video category
  • Edit video category
  • Delete video category
  • Uploaded video management
  • View all uploaded videos
  • View video metadata
  • View video owner information
  • Delete inappropriate uploaded videos
  • Detection request management
  • View all detection requests
  • Filter pending detection requests
  • Filter completed detection requests
  • Filter failed detection requests
  • View full detection detail
  • View frame and frequency analysis
  • Filter real/fake completed results
  • Download detection PDF report
  • Dataset management
  • View real/fake training samples
  • Upload real dataset sample
  • Upload fake dataset sample
  • Delete dataset records
  • Frame extraction settings management
  • Configure frame count
  • Configure frame size
  • Configure face detection
  • Frequency settings management
  • Configure FFT bands
  • Configure noise threshold
  • Configure texture sensitivity
  • Transformer settings management
  • Configure transformer layers
  • Configure attention heads
  • Configure embedding dimension
  • Configure dropout
  • Model training management
  • View model training status
  • View training accuracy and loss
  • Start model training on dataset
  • Update/reload model from disk
  • Test model with sample video
  • Feedback management
  • View user feedback
  • Update feedback status
  • Delete feedback records
  • Contact query management
  • View contact messages
  • Manage reply status
  • Delete contact queries
  • System logs management
  • Login history tracking
  • Upload history tracking
  • Detection history tracking
  • Generate daily activity report
  • Generate monthly activity report

User Features

What end users can do in this application

  • User registration in this final year project
  • User login and logout
  • User dashboard with upload and detection statistics
  • View total uploads
  • View detection count
  • View real and fake detection counts
  • View recent activity
  • View detection pipeline overview
  • Manage user profile
  • Update full name, email, and phone
  • Change account password
  • Upload video for detection
  • Add video title and category
  • Validate video format and size
  • Preview uploaded video metadata
  • Submit uploaded video for deepfake analysis
  • Upload single image for detection
  • View detection result
  • View real/fake classification
  • View confidence score
  • View frame-level analysis
  • View noise pattern analysis
  • View texture anomaly details
  • View frequency pattern analysis
  • View final detection summary
  • Download PDF detection report
  • View uploaded videos
  • Search and filter detection history
  • Delete own detection records
  • Submit user feedback
  • View feedback status
  • Contact support
  • Access upload guidelines
  • Access deepfake awareness content

Other Features

Additional capabilities included in the project

  • Complete deepfake detection source code
  • Suitable for final year project, major project, and minor project
  • Project report content can be prepared from included modules
  • Public Website, User Panel, and Admin Panel included
  • Video and image deepfake detection support
  • Frequency-domain analysis support
  • Transformer-based classification support
  • Confidence score output
  • PDF report download support
  • Dataset upload support
  • Admin model training support
  • SQLite database
  • Flask-SQLAlchemy ORM
  • Flask-Login authentication
  • Flask-WTF form validation
  • Werkzeug password hashing
  • OpenCV video/image processing
  • PyTorch model integration
  • Pillow image handling
  • ReportLab PDF generation
  • Seed data included
  • Upload folders included
  • Dataset folder included
  • Trained model storage folder included
  • No external API required
  • Runs detection locally on the server
  • Useful for viva, source code review, project report writing, and project demonstration

How to Run

Step-by-step setup on your laptop or PC

  1. Open project folder:

    
 
    cd "Deepfake Detection Using Frequency-Domain and Transformer Models"

  2. Create virtual environment on Windows:

    
 
    python -m venv venv
venv\Scripts\activate

  3. Create virtual environment on Linux/macOS:

    
 
    python3 -m venv venv
source venv/bin/activate

  4. Install dependencies:

    
 
    pip install -r requirements.txt

  5. Seed the database:

    
 
    python seed.py

  6. Run the application:

    
 
    python run.py

  7. Open home page:

    
 
    http://localhost:5000/

  8. Open login page:

    
 
    http://localhost:5000/auth/login

  9. Open register page:

    
 
    http://localhost:5000/auth/register
  10. Open user dashboard:

 
http://localhost:5000/user/dashboard
  1. Open admin dashboard:

 
http://localhost:5000/admin/dashboard

Login Credentials

Default demo accounts for testing after setup

Admin Account

  • Username: admin
  • Email: [email protected]
  • Password: admin123
  • Dashboard: http://localhost:5000/admin/dashboard

Recommended Test User

  • Username: john_doe
  • Password: user123

License

Usage terms for academic and personal projects

Related Tags

Search terms and categories for this source code

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