Update README.md

README.md

@@ -1,34 +1,24 @@
----
-license: mit
----# SecureAI: Implementing Robust Watermarking for Model Protection
+# Watermarking in Machine Learning Project
 
-Welcome to SecureAI, a project demonstrating the implementation of watermarking techniques to protect machine learning models from unauthorized use or replication.
+This repository contains code and data to demonstrate watermarking techniques within a machine learning project, specifically for text classification tasks.
 
 ## 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.
+The repository includes the following files and folders:
 
-This project aims to demonstrate:
+- `README.md`: Instructions on implementing watermarking in your project.
+- `watermarking_functions.py`: Functions to embed and detect watermarks within a machine learning model.
+- `model_training.py`: Script for creating and training a text classification model and embedding a watermark into it.
+- `demo_script.py`: Script to demonstrate watermark detection from a model with an embedded watermark.
+- `sample_data/`: Folder containing sample text data for training and testing the model.
+- `LICENSE`: License file specifying terms of usage.
 
-- 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.
+## How to Use
 
-## Key Components
+### 1. Clone the Repository
 
-- **Watermarking Algorithm**: The project implements a watermarking algorithm to embed a unique identifier into the machine learning model.
+Clone this repository to your local machine using the following command:
 
-- **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.
+```bash
+git clone https://huggingface.co/your-username/your-repository-name.git
+cd your-repository-name