Reality Check (Deepfake Detection)

In an age of AI-generated content, seeing is no longer believing. Our system provides deepfakes detection and verifies the authenticity of media content, inspired by my research on deepfake detection

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Inspiration

We’re living in an era where misinformation can spread faster than the truth, especially through video content. Inspired by the urgent need for accessible tools to verify media authenticity, I built DeepFake Detector to empower anyone to validate the trustworthiness of what they see online. Additionally, I’m currently researching deepfake detection in cybersecurity, and I wanted to create something that could help people verify what they’re seeing online.

What it does

The idea is to detect media content that have been AI-generated and gives you a score of how likely it has been manipulated.

How we built it

This project was created entirely within the Lovable platform and OpenAi API as part of the AI Collective Hackathon. Languages: TypeScript, JavaScript

Challenges we ran into

Model Integration: Adapting deepfake models to work efficiently within Lovable’s infrastructure and frame-by-frame analysis (still work in progress)

Accomplishments that we're proud of

It accurately shows a score of AI-generated content and even gives a cautionary advisory.

What we learned

Rapid prototyping with Lovable: I explored how to use Lovable's low-code environment to build and deploy an AI tool quickly.

Blending multiple detection techniques: I learned how combining deepfake detection, visual inconsistency analysis, and metadata forensics strengthens reliability.

UX for AI trust: Presenting a “fake-likelihood score” in a user-friendly, explainable way was more challenging—and more important—than expected.

Responsible AI design: It's not just about saying a video is fake, it's about showing why, and letting users interpret that responsibly.

What's next for Reality Check

Browser Extension: We're building a Chrome/Firefox extension that lets users verify videos directly on platforms like YouTube and X (Twitter).

Real-time scanning: Future versions will allow live video analysis via webcam or screen share, audio analysis and frame-by-frame analysis.

Platform integrations: We're exploring partnerships with content platforms to offer integrated video authentication tools and tweak the models for forensic analysis.

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