Project Face Off : Catching Digital Lies

👋 , What if you could spot a fake face before it fools you?

With deepfakes becoming more realistic by the day, I wanted to do something about it. So, I built a Deepfake Detection Tool — designed to analyze videos, especially from Instagram, and reveal the truth behind digital faces.

This blog takes you through my project journey — from idea to design to code. Buckle up, because it’s been one eye-opening ride!

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🧠 The Idea: Why Deepfakes?

Deepfakes are cool... until they’re dangerous.

With the rise of AI-generated content, it’s getting harder to distinguish real from fake. This inspired me to create something that could help identify manipulated videos, especially those shared on platforms like Instagram, where visual content is everything.

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💡 The Solution: My Deepfake Detection App

Built using Python, OpenCV, and deep learning models

Designed a clean, user-friendly interface on Figma (Link below 👇🏻)

Takes a video file, analyzes faces frame by frame

Uses pre trained models to detect signs of manipulation

Provides a confidence score and detection result

💻View My Figma Design

📁 View Project Code Files

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🛠️ How I Built It: The Tech Stack

Frontend: Figma UI design (for website and app interface)

Backend: Python, Flask (or Streamlit – based on your final setup)

Model: Deepfake detection using CNN / pretrained network

Extras: OpenCV for video processing, NumPy, TensorFlow/Keras for inference

The hardest part!?

Video preprocessing and face tracking across frames. But once I got that working, the detection flow felt smooth and logical.

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🌐 The Vision

I wanted this project to be more than just code.

I imagined it as a tool that could be used by content moderators, researchers, or even social media users who want to verify content before sharing.

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🧪 Key Features

🎥 Upload any video (Instagram-reel-ready)

🧠 AI-based deepfake analysis

📊 Real-time feedback with confidence scores

🖥️ Clean, minimal interface design (made for accessibility)

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🧩 What I Learned

Real-world AI projects require more than just models — design and usability matter

Data preprocessing is the unsung hero of ML pipelines

Patience and version control save you during debugging 😅

Deepfake detection isn’t solved — it’s evolving

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🔚 Conclusion

This was more than a project — it was a chance to work on something that matters.

I’m excited to keep improving it, maybe even scale it into a web app or integrate it with browser extensions someday.

If you're interested in deepfake detection, AI, or want to collaborate — let’s connect!

🔗 Got feedback or ideas?

✨Drop a comment - I’d love to connect with fellow AI enthusiasts!✨