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| 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. |