pySTC

pySTC is a Python interface for Syndrome-Trellis Codes (STC), a method used in steganography to minimize embedding distortion when hiding information within digital media. STCs are linear convolutional codes represented by a parity-check matrix, allowing efficient embedding while preserving the quality of the cover medium.

This library is based on the C++ implementation of Syndrome-Trellis Codes available at Binghamton University's STC repository. We extend our gratitude to the original authors for their contribution to the field of steganography.

Installation

To install pySTC, simply use pip:

pip install pystcstego

This will install the latest version of pySTC along with its dependencies.

Usage

Below is an example of how to use pySTC for embedding and extracting messages using Syndrome-Trellis Codes. In this example, we have performed the cost calculation using HILL as defined in the paper A New Cost Function for Spatial Image Steganography by Bin Li, Ming Wang, Jiwu Huang and Xiaolong Li.

import pystc
import numpy as np
import imageio.v3 as iio
import warnings
from scipy import signal

warnings.filterwarnings('ignore', category=RuntimeWarning)

input_image = 'image.pgm'

def HILL(I):
    H = np.array(
       [[-1,  2, -1],
        [ 2, -4,  2],
        [-1,  2, -1]])
    L1 = np.ones((3, 3)).astype('float32')/(3**2)
    L2 = np.ones((15, 15)).astype('float32')/(15**2)
    costs = signal.convolve2d(I, H, mode='same')  
    costs = abs(costs)
    costs = signal.convolve2d(costs, L1, mode='same')  
    costs = 1/costs
    costs = signal.convolve2d(costs, L2, mode='same')  
    costs[costs == np.inf] = 1
    return costs

I = iio.imread(input_image)

costs = HILL(I)
seed = 32 # secret seed

message = "Hello World".encode()
stego = pystc.hide(message, I, costs, costs, seed, mx=255, mn=0)

message_extracted = pystc.unhide(stego, seed)
print("Extracted:", message_extracted.decode())

Acknowledgments

Part of the C/C++ code used by HStego comes from the Digital Data Embedding Laboratory. We sincerely appreciate their work in developing and sharing this technology.

These methods are described in the following papers:

• Minimizing Embedding Impact in Steganography using Trellis-Coded Quantization by Tomas Filler, Jan Judas and Jessica Fridrich.

• Minimizing Additive Distortion Functions With Non-Binary Embedding Operation in Steganography by Tomas Filler and Jessica Fridrich.

• Minimizing Additive Distortion in Steganography using Syndrome-Trellis Codes by Tomas Filler, Jan Judas and Jessica Fridrich.

License

This project is licensed under the MIT License. See the LICENSE file for more details.