\[ \begin{align}\begin{aligned}\newcommand\blank{~\underline{\hspace{1.2cm}}~}\\% Bold symbols (vectors) \newcommand\bs[1]{\mathbf{#1}}\\% Poor man's siunitx \newcommand\unit[1]{\mathrm{#1}} \newcommand\num[1]{#1} \newcommand\qty[2]{#1~\unit{#2}}\\\newcommand\per{/} \newcommand\squared{{}^2} \newcommand\cubed{{}^3} % % Scale \newcommand\milli{\unit{m}} \newcommand\centi{\unit{c}} \newcommand\kilo{\unit{k}} \newcommand\mega{\unit{M}} % % Percent \newcommand\percent{\unit{\%}} % % Angle \newcommand\radian{\unit{rad}} \newcommand\degree{\unit{{}^\circ}} % % Time \newcommand\second{\unit{s}} \newcommand\s{\second} \newcommand\minute{\unit{min}} \newcommand\hour{\unit{h}} % % Distance \newcommand\meter{\unit{m}} \newcommand\m{\meter} \newcommand\inch{\unit{in}} \newcommand\foot{\unit{ft}} % % Force \newcommand\newton{\unit{N}} \newcommand\kip{\unit{kip}} % kilopound in "freedom" units - edit made by Sri % % Mass \newcommand\gram{\unit{g}} \newcommand\g{\gram} \newcommand\kilogram{\unit{kg}} \newcommand\kg{\kilogram} \newcommand\grain{\unit{grain}} \newcommand\ounce{\unit{oz}} % % Temperature \newcommand\kelvin{\unit{K}} \newcommand\K{\kelvin} \newcommand\celsius{\unit{{}^\circ C}} \newcommand\C{\celsius} \newcommand\fahrenheit{\unit{{}^\circ F}} \newcommand\F{\fahrenheit} % % Area \newcommand\sqft{\unit{sq\,\foot}} % square foot % % Volume \newcommand\liter{\unit{L}} \newcommand\gallon{\unit{gal}} % % Frequency \newcommand\hertz{\unit{Hz}} \newcommand\rpm{\unit{rpm}} % % Voltage \newcommand\volt{\unit{V}} \newcommand\V{\volt} \newcommand\millivolt{\milli\volt} \newcommand\mV{\milli\volt} \newcommand\kilovolt{\kilo\volt} \newcommand\kV{\kilo\volt} % % Current \newcommand\ampere{\unit{A}} \newcommand\A{\ampere} \newcommand\milliampereA{\milli\ampere} \newcommand\mA{\milli\ampere} \newcommand\kiloampereA{\kilo\ampere} \newcommand\kA{\kilo\ampere} % % Resistance \newcommand\ohm{\Omega} \newcommand\milliohm{\milli\ohm} \newcommand\kiloohm{\kilo\ohm} % correct SI spelling \newcommand\kilohm{\kilo\ohm} % "American" spelling used in siunitx \newcommand\megaohm{\mega\ohm} % correct SI spelling \newcommand\megohm{\mega\ohm} % "American" spelling used in siunitx % % Inductance \newcommand\henry{\unit{H}} \newcommand\H{\henry} \newcommand\millihenry{\milli\henry} \newcommand\mH{\milli\henry} % % Power \newcommand\watt{\unit{W}} \newcommand\W{\watt} \newcommand\milliwatt{\milli\watt} \newcommand\mW{\milli\watt} \newcommand\kilowatt{\kilo\watt} \newcommand\kW{\kilo\watt} % % Energy \newcommand\joule{\unit{J}} \newcommand\J{\joule} % % Composite units % % Torque \newcommand\ozin{\unit{\ounce}\,\unit{in}} \newcommand\newtonmeter{\unit{\newton\,\meter}} % % Pressure \newcommand\psf{\unit{psf}} % pounds per square foot \newcommand\pcf{\unit{pcf}} % pounds per cubic foot \newcommand\pascal{\unit{Pa}} \newcommand\Pa{\pascal} \newcommand\ksi{\unit{ksi}} % kilopound per square inch \newcommand\bar{\unit{bar}} \end{aligned}\end{align} \]

Oct 24, 2024 | 1165 words | 12 min read

9.1.1. Materials

Project Description

In this project, you will explore hiding and recovering secret messages in images. We will use a combination of encryption and steganography to achieve this. Encryption is the process of converting a message into a secret form so that only the intended recipient can read it. Steganography is the practice of concealing a message within another message or medium to hide the fact that a message is being sent.

Your work will be evaluated via in-class demonstrations three times throughout the project, at checkpoints 1 and 2, and during the final demo. Each time, your team will present your progress to a member of the instructional team. Details of what is required at each checkpoint are provided below.

By working on this project, you will gain hands-on experience with encryption, working with binary data, and processing images. You will learn how data can be hidden inside images and then safely extracted. These skills are very useful in areas like cybersecurity, digital communications, and computer engineering.

Understanding Bytes, Pixels, and LSB Encoding

In this project, you’re tasked with hiding an encrypted message inside an image and then retrieving it. To do this effectively, it’s important to understand some key concepts: bytes, pixels, and how we can use the Least Significant Bit (LSB) technique to encode binary data into an image.

Bytes and Binary Data

Everything stored on a computer is ultimately represented in binary form, which means it’s made up of bits–zeros and ones. A group of 8 bits makes up a byte. A single byte can represent 256 different values. We often use integers from 0 to 255 to represent these values starting with “0” for “00000000” and ending with “255” for “11111111”.

Text characters are also stored in binary form. For example, the letter “H” in ASCII, is represented with a single byte as “01001000”. This is equivalent to the integer 72. So we say that the letter “H” has ASCII code point 72. Converting each character in a longer message, such as “Hello World”, to binary form gives us “01001000 01100101 01101100 01101100 01101111 00100000 01010111 01101111 01110010 01101100 01100100”.

Binary	Integer
00000000	0
00000001	1
00000010	2
…	…
11111110	254
11111111	255

Pixels and Images

An image is made up of tiny dots called pixels. Each pixel represents a small portion of the image and has its own color. In digital images, colors are often represented using three values: Red, Green, and Blue (RGB). The intensity of each of these color values is typically stored as a byte, so that one pixel can be represented by three bytes.

For example, a pure red pixel might have the RGB values (255, 0, 0), where 255 is the maximum value for the red channel, and 0 is the minimum for green and blue.

Least Significant Bit (LSB) Encoding

The Least Significant Bit is the rightmost bit in a binary number. For example, in the byte 01000001, the least significant bit is the last ‘1’. Changes to the least significant bit are often imperceptible to the human eye. The idea behind LSB encoding is to use this bit in each pixel’s color value to hide information unrelated to the image without significantly altering the image’s appearance.

Checkpoint 1 overview: Decoding an Encoded Image

In this first checkpoint, your objective is to recover a hidden plaintext message from an encoded image provided to your team. Before the checkpoint, you will be supplied with a zip file containing two images based on Beau (Prof. Cole’s brown dog). Each team will receive unique images with a different message embedded. The images include grayscale and color reference images (beau_gry.png and beau_col.png) and their corresponding images with the encoded message (beau_section_team.png and beau_gry_section_team.png).

During this checkpoint, your team will need to:

1. Display an Encoded Image:

   Your code should be able to load and display either the color or grayscale image (instructor’s choice) that contains the encoded message.

2. Extract the binary message:

   Recover the binary message embedded within the image using appropriate decoding techniques.

3. Recover the plaintext message:

   Convert the binary message back into text to reveal the hidden plaintext message.

Checkpoint 2 overview: Encryption and Encoding into an Image

In the second checkpoint, you will encrypt a given secret message and encode it into an image. Before the checkpoint, you will be provided a zip file containing images based on “Bear” (Prof. Cole’s black and white dog). Each team will receive different messages and encryption keys or shifts. The images include reference images and images with messages encrypted using the Caesar, XOR, and Vigenère ciphers in both grayscale and color formats.

During this checkpoint, your team will need to:

1. Display Image Differences:

   Show the difference between the original bear image and the provided encoded bear image.

2. Encrypt the secret message:

   Your team will select one cipher from Caesar, XOR, or Vigenère to encrypt the provided secret message and produce the ciphertext. The teaching team will provide the secret message and the encryption key or shift.

3. Encode the message into an image:

   Embed the encrypted binary message into the provided reference image (instructor’s choice of either bear_gry.png or bear_col.png). Once created, your encoded image and our encoded image should be identical.

Final Demo overview: Complete Encryption and Decryption Process

For the final demo, you will perform both encryption and encoding at a randomized location, as well as decoding and decryption from a random location. Before the demo, you will be provided a zip file containing images featuring both Beau and Bear. Each team will have different messages, encryption keys or shifts, and offsets. The images include reference images and images with messages encoded at randomized locations using different ciphers.

During the final demo, your team will need to:

1. Encrypt a message and encode it into an image:

   Use an instructor specified cipher, key or shift, and bit offset to encrypt the secret message, and embed the binary message into the image. Then compare your encoded image with the provided encoded image.

2. Decode and decrypt a message hidden in an image:

   Use a different instructor specified cipher and key or shift, to recover, decrypt, and display the secret message hidden in the image.

Team Size Adjustment for Cipher Choices

• Teams of 4 or More Members: Implement all three ciphers–Caesar, XOR, and Vigenère.

• Teams of 3 Members: Implement XOR and Vigenère ciphers.

• Teams of 2 Members: Implement one cipher (other than Caesar).

Important Notes

For this project, you are only permitted to use the Python standard library and the third party modules numpy and matplotlib. You may not use PIL or OpenCV for image processing. You are also not allowed to use any external libraries for encryption or decryption. You must implement the Caesar, XOR, and Vigenère ciphers from scratch.

The image filenames use the following convention.

• Suffixes _p, _c, _x, or _v, denote the type of cipher used—Plaintext, Caesar, XOR, or Vigenère, respectively.

• The characters _col or _gry in the filename indicates whether the image is colored or grayscale.

To ensure your code is organized and easy to understand, structure your program using functions. Modular code allows for better collaboration within your team, making it easier to discuss ideas, debug issues, and integrate different parts of the project seamlessly. Working together, you can design and implement user-defined functions that perform specific tasks, accept necessary inputs, and return desired outputs. Avoid dividing the tasks to work individually; instead, engage in collective problem-solving to enhance your learning experience.