Bpcs

Abstract

1. Introduction

The goal of steganography is to hide a message in plain sight. BPCS is a method to embed a message in an image by replacing all "complex" blocks of pixels in the image with portions of our message. It turns out that portions of the image with high complexity can be entirely removed (or in this case, replaced with our message) without changing the appearance of the image at all. Because most blocks of pixels are complex (i.e., with complexity above some threshold, alpha), you can usually replace around 45% of an image with a hidden message. Below, the 300x300 image on the right contains the text of an entire novel, while still looking virtually identical to the vessel image on the left.

This is an implementation of the method discussed in: Kawaguchi, Eiji, and Richard O. Eason. "Principles and applications of BPCS steganography." In Photonics East (ISAM, VVDC, IEMB), pp. 464-473. International Society for Optics and Photonics, 1999. (pdf)

$ python -m bpcs.bpcs encode -i examples/vessel.png -m examples/message.txt -a 0.45 -o examples/encoded.png

2. Objective

digital image steganography using BPCS (Bit-Plane Complexity Segmentation Steganography)

This project demonstrates how Python can be applied to a real-world Cybersecurity & Ethical Hacking problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/mobeets/bpcs.git
cd bpcs

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add logging and alert notifications (email / Telegram)
• Write a threat model document for the tool
• Package it with Docker for safe lab testing

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which threat or attack does this project defend against?
2. What detection or protection technique is used and what are its limits?
3. How are false positives and false negatives handled?
4. Which cryptographic algorithms or security standards are involved?
5. What legal and ethical rules apply when testing a tool like this?

9. Source Code & License

This project is developed by mobeets and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.