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