Team Name
Team 404
Hackathon Track
AI/ML
ps-number
4
Email Address
aryankhandare2005@gmail.com
Email Addresses of Team Members
vrusha2107@gmail.com, bhaktirathod10@gmail.com, meetmak231@gmail.com
Project Description
We built a Multimodal Deepfake Detection & Trust Engine that analyzes videos to determine whether they are real or fake using AI.
The system allows users to upload media, processes it through a deep learning model, and returns a real-time authenticity score along with confidence levels.
It includes:
Video-based deepfake detection using frame-level analysis
Confidence scoring and risk classification
A web interface for easy upload and instant results
Backend API for integration with other platforms
Overall, it acts as a trust layer for digital media, helping users verify authenticity.
Inspiration behind the Project
With the rapid rise of generative AI, deepfakes are becoming more realistic and harder to detect. This creates serious risks like misinformation, identity fraud, and loss of trust in digital content.
We chose this problem because it has real-world impact — especially in social media, news, and financial systems.
Our goal was to build a system that not only detects deepfakes but also provides a confidence score, so users can make informed decisions instead of blindly trusting media.
We focused on making the solution practical, scalable, and easy to use, rather than just a research model.
Tech Stack
FRONTEND
React → building user interface
Vite → fast development & project setup
Tailwind CSS → styling and responsive design
Axios → sending API requests to backend
BACKEND
FastAPI → creating API for prediction
Uvicorn → running backend server
MACHINE LEARNING
PyTorch → building and running deep learning model
ResNet18 → model for deepfake detection
Torchvision → image transformations & model utilities
DATA PROCESSING
OpenCV → video reading & frame extraction
NumPy → numerical operations (averaging scores)
DATASET
FF++ / Celeb-DF → training data for real vs fake videos
Project Repo
https://github.com/AryanKhandare/Trust_Engine.git
Demo Video
https://drive.google.com/file/d/1Raq2bOGUseUr4vRbliiTscIWcEOo5-ao/view?usp=sharing
Presentation Link
https://canva.link/3ng7obts8vb9ds3
Anything Else?
NA
Rules and Code of Conduct
Team Name
Team 404
Hackathon Track
AI/ML
ps-number
4
Email Address
aryankhandare2005@gmail.com
Email Addresses of Team Members
vrusha2107@gmail.com, bhaktirathod10@gmail.com, meetmak231@gmail.com
Project Description
We built a Multimodal Deepfake Detection & Trust Engine that analyzes videos to determine whether they are real or fake using AI.
The system allows users to upload media, processes it through a deep learning model, and returns a real-time authenticity score along with confidence levels.
It includes:
Video-based deepfake detection using frame-level analysis
Confidence scoring and risk classification
A web interface for easy upload and instant results
Backend API for integration with other platforms
Overall, it acts as a trust layer for digital media, helping users verify authenticity.
Inspiration behind the Project
With the rapid rise of generative AI, deepfakes are becoming more realistic and harder to detect. This creates serious risks like misinformation, identity fraud, and loss of trust in digital content.
We chose this problem because it has real-world impact — especially in social media, news, and financial systems.
Our goal was to build a system that not only detects deepfakes but also provides a confidence score, so users can make informed decisions instead of blindly trusting media.
We focused on making the solution practical, scalable, and easy to use, rather than just a research model.
Tech Stack
FRONTEND
React → building user interface
Vite → fast development & project setup
Tailwind CSS → styling and responsive design
Axios → sending API requests to backend
BACKEND
FastAPI → creating API for prediction
Uvicorn → running backend server
MACHINE LEARNING
PyTorch → building and running deep learning model
ResNet18 → model for deepfake detection
Torchvision → image transformations & model utilities
DATA PROCESSING
OpenCV → video reading & frame extraction
NumPy → numerical operations (averaging scores)
DATASET
FF++ / Celeb-DF → training data for real vs fake videos
Project Repo
https://github.com/AryanKhandare/Trust_Engine.git
Demo Video
https://drive.google.com/file/d/1Raq2bOGUseUr4vRbliiTscIWcEOo5-ao/view?usp=sharing
Presentation Link
https://canva.link/3ng7obts8vb9ds3
Anything Else?
NA
Rules and Code of Conduct