README.md

Steganography

Steganography is for hiding text in the image without changeing image look with openCV and Numpy Library

Summery :

This project first we calculate image features like height width and size then convert the text into binary text then check if the image is big enough to store the text ? then extend the 0 bit to fit the text into image size. seprate the image into Red Green and Blue frame then seprate the text into 3 part then for every frame we remove the LSB bit of a pixel then replace it with our bit from the text then do this for all pixels then our text is decoded into image. now we can do it reverse for encodeing text from image.

Tech 🛠️ Languages and Tools :

     

Tutorial

Step1: Install Librarys

We need to have installed Python, OpenCV and Numpy

for windows and ubuntu we need install python, OpenCV and Numpy

and for Google colab just install OpenCV

install OpenCV

!pip install opencv-python

install Numpy

!pip install numpy

Step2: Import Librarys

import opencv,numpy library and cv2_imshow for showing picture in google colab

import cv2
import numpy as np
from google.colab.patches import cv2_imshow

Step3: Load Image

Upload your image into google colab or for local device using a local image.

found your image location and copy the path

image_path = "/content/Wallpaper.jpg"
image = cv2.imread(image_path)
cv2_imshow(image)

Step4: Load text data

here you can enter your text to hide it in image

text = """here is your text"""

Step5: Convert text to binary

you need to convert your text into binary text

def text_to_bin(text):
    binary = ''.join(format(ord(char), '08b') for char in text)
    return binary
binary_text = text_to_bin(text)
print(binary_text)

here is the sample of binary converted:

010000000110100101101101

Step6: Calculate size of image

now you need to know what is your image size and how much it can hold data within

height, width, channels = image.shape
image_size = height*width
max_chars = int((channels*image_size)/8)

print("image height : " + str(height))
print("image width : " + str(width))
print("image size : " + str(image_size))
print("max charechter can hide in picture : " + str(max_chars))
print("image can hide : " + str((channels*image_size)/80) + " Words")

here is some information about our image :

image height : 720

image width : 1280

image size : 921600

max charechter can hide in picture : 345600

image can hide : 34560.0 Words

Step7: Extend binary bits

Here you should change the format of your text into image to fit the text to image

if your text is to big for your image here you see the error

def extend_binary_text(image_size, binary_text, max_chars):
    needed_bits = len(binary_text)
    if(needed_bits > max_chars*8):
        print("text size is too high")
        return

    extended_binary_text = binary_text.ljust(max_chars*8, '0')
    return extended_binary_text
extended_binary_text = extend_binary_text(image_size, binary_text, max_chars)

Step8: Spereate binary text into channel parts

Because we have RGB image for using maximum space from the image we should separate the text into 3 part

def divide_string_parts(text, channels):
    part_length = len(text) // channels
    text_parts = []
    
    for i in range(channels):
        part_start = part_length * i
        part_end = part_length * (i + 1)
        text_parts.append(text[part_start:part_end])
    
    return text_parts
text_parts = divide_string_parts(extended_binary_text, channels)

Step9: Seperate image to channel parts

Sperate image into Red Green and Blue frame

if channels == 3:
    blue_channel, green_channel, red_channel = cv2.split(image)
    blue_matrix = np.array(blue_channel)
    green_matrix = np.array(green_channel)
    red_matrix = np.array(red_channel)

Step10: Decode text into image

Here is our main function for decoding data into image in here we remove the first bit(LSB) of a pixel then replace it with our data then do it for all pixels

def decimalToBinary(n): 
    return format(n, '08b')

def binaryToDecimal(n):
    return int(n,2)

counter = 0
for i in range(height):
    for j in range(width):
        red_matrix[i,j] = binaryToDecimal(decimalToBinary(red_matrix[i,j])[0:7] + text_parts[0][counter])
        green_matrix[i,j] = binaryToDecimal(decimalToBinary(green_matrix[i,j])[0:7] + text_parts[1][counter])
        blue_matrix[i,j] = binaryToDecimal(decimalToBinary(blue_matrix[i,j])[0:7] + text_parts[2][counter])
        counter += 1

Step11: Build image

We need to gather Red Green and Blue Frame and Biuld our image.

reconstructed_image = cv2.merge([blue_matrix, green_matrix, red_matrix])
cv2.imwrite("/content/ImageWithData.png", reconstructed_image)
cv2_imshow(reconstructed_image)

if you notice you cant see any changes from the picture.

Step12: Encode text from image

Encode the image with reading the image by 8bit and extract the text.

def bin_to_text(binary):
    text = ''.join(chr(int(binary[i:i+8], 2)) for i in range(0, len(binary), 8))
    return text

def cut_binary_at_8_zeros(binary_str):
    for i in range(0, len(binary_str), 8):
        chunk = binary_str[i:i+8]
        if chunk == '00000000':
            return binary_str[:i]
    return binary_str

def decode_text_from_image(image_path):
    image = cv2.imread(image_path)
    blue_channel, green_channel, red_channel = cv2.split(image)

    blue_matrix = np.array(blue_channel)
    green_matrix = np.array(green_channel)
    red_matrix = np.array(red_channel)

    text1 = ""
    text2 = ""
    text3 = ""
    counter = 0
    for i in range(height):
        for j in range(width):
            text1 = text1 + decimalToBinary(red_matrix[i,j])[-1]
            text2 = text2 + decimalToBinary(green_matrix[i,j])[-1]
            text3 = text3 + decimalToBinary(blue_matrix[i,j])[-1]
            counter += 1
    concated_binary_text = text1 + text2 + text3
    cuted_binary_text = cut_binary_at_8_zeros(concated_binary_text)
    return bin_to_text(cuted_binary_text)

decode_text_from_image("/content/ImageWithData.png")

after running this method you can see the hided text