DeepFake Detection-Nillion

[SUGAMANIRAJ]

Show and tell project type

Hackathon Project Submission

Github Repo Link

https://github.com/SUGAMANIRAJ/quickstart

Video Walkthough Link

https://youtu.be/UQKbx1VWHQA

Live Versel Link

https://quickstart-git-main-sugamanirajs-projects.vercel.app/

Project Description

Creating a cutting-edge deepfake detection model leveraging advanced machine learning algorithms to analyze video inputs and accurately differentiate between genuine and falsified content.

Our goal is to ensure the integrity of digital media by providing a robust and efficient solution for identifying and mitigating the spread of deceptive deepfake videos.

What problems does your project solve? How does it preserve privacy for users?

Problem It Solves

The Deepfake Detector solves the problem of identifying fake videos that have been altered using deepfake technology. As deepfakes become more common, they can be used to spread false information, damage reputations, or deceive people. The Deepfake Detector helps users and organizations check if a video is real or fake, ensuring that only authentic content is trusted and shared. This helps prevent the spread of misleading videos and protects people from being fooled by fake content.

Blind Computation

Blind computation is used to ensure the privacy of the results given by the deepfake detection model. Every result is considered sensitive, regardless of whether it indicates a video is real or fake.

How does the project use Nillion? Describe and link to any Nada programs

Our project uses Nillion in the computational part of the deepfake detection model. Since the computation is crucial and involves individual privacy, Nillion plays a key role in safeguarding this aspect.

Is there anything else you want to share?

No response

Optional - Link your project and team members' social handles

• SUGAMANIRAJ N - LinkedIn
• TEJESHWAR M - LinkedIn

Optional - Team ETH Address(es)

No response