---
license: mit
---# SecureAI: Implementing Robust Watermarking for Model Protection

Welcome to SecureAI, a project demonstrating the implementation of watermarking techniques to protect machine learning models from unauthorized use or replication.

## Overview

Machine learning models are vulnerable to intellectual property theft or unauthorized replication, posing a challenge for model developers and organizations. SecureAI addresses this concern by embedding a unique signature or watermark into the model, enabling verification of its authenticity and protecting it from misuse.

This project aims to demonstrate:

- Implementation of a watermarking algorithm for model protection.
- Embedding a watermark into a machine learning model without compromising performance.
- Evaluating the robustness of the watermark against various attacks and model modifications.
- Detection and extraction of the watermark for verification purposes.

## Key Components

- **Watermarking Algorithm**: The project implements a watermarking algorithm to embed a unique identifier into the machine learning model.

- **Model Training and Embedding**: Train a sample machine learning model and embed a watermark using the implemented algorithm.

- **Robustness Testing**: Assess the robustness of the watermark by conducting tests such as model fine-tuning, performance evaluation, and watermark extraction.

- **Demonstration**: A demonstration showcasing watermark detection and extraction from the model to verify its presence and authenticity.

## Usage

To reproduce the watermarking process or experiment with watermark detection:

1. **Requirements**: Ensure you have the necessary dependencies installed (Python, TensorFlow/PyTorch, etc.).
2. **Clone the Repository**: Clone this repository to your local machine.
3. **Follow Instructions**: Follow the instructions in the code or README files to run the watermarking algorithm, embed the watermark, and perform detection/extraction.