Update README.md

Author: Suvadip-sana

OpenCV Projects/Steganography/README.md

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+# Image Steganography Using Two LSB Embedding
+
+This project implements a simple image steganography technique using the least significant bits (LSB) embedding method. The goal is to hide a secret image inside a cover image by replacing the least significant bits of the cover image with the bits from the secret image. The process includes both embedding and extraction functionalities.
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Features](#features)
+- [Requirements](#requirements)
+- [Usage](#usage)
+  - [Embedding Process](#embedding-process)
+  - [Extraction Process](#extraction-process)
+  - [MSE and PSNR Calculation](#mse-and-psnr-calculation)
+- [Output](#output)
+
+## Introduction
+
+Steganography is a technique of hiding information in other digital media. In this project, we hide a 64x64 grayscale secret image inside a 512x512 grayscale cover image using the 2 least significant bits of the cover image's pixels. This process ensures minimal distortion in the cover image while allowing the secret image to be extracted later.
+
+## Features
+- Embeds a secret image into a cover image using the two least significant bits (LSB).
+- Extracts the hidden secret image from the stego image.
+- Computes Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) between images.
+
+## Requirements
+
+- Python 3.x
+- OpenCV (`cv2`)
+- NumPy
+- Matplotlib
+
+You can install the required libraries using the following command:
+
+```bash
+pip install numpy opencv-python matplotlib
+```
+
+## Usage
+- ### Embedding Process
+  The `Embedding_two_lsb` function hides the secret image into the cover image using the LSB method. The cover image and secret image are resized to 512x512 and 64x64 respectively before embedding.
+- ### Extraction Process
+  The `Extraction_two_lsb` function retrieves the hidden secret image from the stego image.
+- ### MSE and PSNR Calculation
+  The `mse_psnr` function calculates the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) between two images. These metrics help quantify the distortion between the original and stego images, and      the original and extracted secret images.
+
+## Output
+The output includes:
+
+- The cover image and secret image displayed using Matplotlib.
+- The stego image after embedding the secret image.
+- The extracted secret image after applying the extraction algorithm.
+- The MSE and PSNR values.
+- ### Sample Output (PSNR):
+
+```
+MSE between Cover Image and Stego Image: 0.1834
+PSNR: 55.4957
+
+MSE between Secret Image and Extracted Image: 0.0
+PSNR: -999 (Perfect recovery, no error)
+```
+