KAVACH: Proactive Image Integrity & Ownership Security Framework

Project Overview

Kavach is a proactive, camera-integrated computer vision and cryptographic framework designed to secure digital images at the point of capture, protecting individuals from unauthorized AI edits, deepfakes, and privacy violations.

The Completion Arc: Old vs. New

This repository represents a high-speed sprint to transform a theoretical research concept into a live, executing software prototype.

Developer Context: As a 20-year-old student currently utilizing the summer break before university admissions officially begin for my first year of college, this project marks my very first practical experience working with GitHub. Because I do not currently own a laptop, I had to learn version control, navigate coding environments, and build these low-level computer vision pipelines under highly constrained device access.

• The Old Version: Originally, Kavach existed only as a theoretical architectural design paper (kavach_2.pdf). It outlined a conceptual vision for multi-layered validation using pixel structures, but had no functional codebase, execution scripts, or live automation.
• The New Version: During this hackathon, the entire core data pipeline was successfully brought to life in a functional Python prototype (Kavach_core.py and kavach_verify.py), utilizing OpenCV and NumPy to ingest raw portrait images, isolate camera sensor grain via Laplacian noise filtering, and securely lock assets with SHA-256 cryptographic salts.

Link to Code

You can explore the complete codebase, review the sample assets, and inspect the project architecture here:
https://github.com/TechSakhi/KAVACH-Image-Security

How the Prototype Works (Sample Assets Included)

The repository includes pre-loaded sample assets so anyone can test the verification engine immediately:

• sample.jpg: The original, untouched master image.
• sample_edited.jpg & sample_edited1.jpg: Modified test versions to simulate digital tampering.

Running python kavach_verify.py inside the repository will automatically compare these files, extract their matrix layers, and output the exact tampering verification status directly in the terminal.

Technology Stack

• Python 3
• OpenCV (cv2)
• NumPy
• Built-in Cryptography (hashlib & os)

Future Roadmap

1. AI Edit Classification: Integrating machine learning layers to automatically categorize localized image manipulations into specific modification buckets.
2. Hardware Camera API Integration: Compiling the framework into native libraries running directly on mobile camera data-streams.

3. Cloud Ledger & Monitoring Ecosystem: Deploying lightweight background web-scrapers to match online images against a secure master hash registry.

What are your thoughts on shifting image security directly to the camera level? As an incoming first-year student building my very first project, I would love to hear feedback, suggestions, or questions from experienced developers in the comments below!