Stegosphere

A flexible, highly modular steganography and steganalysis library for image, audio, ttf, multiple file and all NumPy-array-readable steganography, including encryption and compression.

It is meant to be usable for research by combining steganography and steganalysis, see Research toolbox.

General usage
Image steganography
Audio steganography
ttf steganography
Multimedia steganography
File handling
Compression and Encryption
Additional parameters
More file types
Research toolbox
Contributing

General

The library was developed to allow for generalisation and compatability of different steganographical methods across file types. The base steganography classes define steganography on top of numpy arrays, while the implementations for different file types primarily aid in converting between the file type and numpy arrays.

Currently, methods for image, audio, ttf and multi-file steganography are implemented.

Image steganography

For image steganography, LSB (Least Significant Bit), PVD (Pixel Value Differencing) and IWT (Integer Wavelet Transform) steganography are currently available.

The example below loads an image, randomly distributes the message across the image using a seed and saves it.

from stegosphere import image

img = image.LSB('image.png')
img.encode('Encoded message!', seed=42, method='delimiter')
img.save('stego_image.png')

steg_img = image.LSB('stego_image.png')
decoded_bits = steg_img.decode(seed=42, method='delimiter', compress=False)
print(image.binary_to_data(decoded_bits))
#Expected output: 'Encoded message!'

For additional parameters, see the chapter on parameters.

Audio steganography

For audio steganography, LSB (Least Significant Bit), FVD (Frequency Value Differencing) and IWT (Integer Wavelet Transform) steganography are currently available. The example below loads an audio and encodes the file image.png into the audio. The image is then recovered and saved.

from stegosphere import audio

wav = audio.FVD('audio.wav')
bin_image = audio.file_to_binary('image.png')
wav.encode(bin_image)
wav.save('steg_audio.wav')

steg_wav = audio.LSB('steg_audio.wav')
audio.binary_to_file(steg_wav.decode(), 'recovered_image.png')

For additional parameters, see the chapter on parameters.

ttf steganography

For ttf steganography, LSB (Least Significant Bit) and Custom Table steganography are currently available.

The example below stores a string into a custom created table within the TTF file.

from stegosphere import ttf

font = ttf.CustomTable('the_font.ttf')
font.encode('Encoded message!', table_name='STEG')
font.save('stegano_font.ttf')

recover_font = ttf.CustomTable('stegano_font.ttf')
print(recover_font.decode(table_name='STEG'))

Multifile steganography

It is also possible to divide the payload across different files. Different methods and parameters can be used for each file where data is being encoded.

from stegosphere import multimedia

data = file_to_binary('encode.png')

lsb_img = image.LSB('cover_image.png')
fvd_audio = audio.FVD('cover_audio.wav')

#Define the custom encoding functions
image_encode = lambda message: lsb_img.encode(message, seed=42, method='delimiter')
audio_encode = lambda message: fvd_audio.encode(message, seed=21)

#Encode the data evenly across the image and audio,
#with the data being randomly distributed using a seed.
split_encode(data, [image_encode,audio_encode], seed=100)

lsb_img.save('multimedia_stego.png')
fvd_audio.save('mutlimedia_stego.wav')

decode_lsb_img = image.LSB('multimedia_stego.png')
decode_fvd_audio = audio.FVD('multimedia_stego.wav')

image_decode = lambda: decode_lsb_img.decode(seed=42, method='delimiter')
audio_decode = lambda: decode_lsb_audio.decode(seed=21)

output = split_decode([image_decode, audio_decode], seed=100)

print(output==data)

The payload can be distributed evenly (default setting), using weighted distribution or roundrobin.

File handling

Several functions for file handling are provided.

stegosphere.file_to_binary(path) --> converts any file into binary for encoding.

stegosphere.binary_to_file(binary_data, output_path) --> saves binary back into file format.

stegosphere.data_to_binary(data) --> converts any string into binary for encoding.

stegosphere.binary_to_data(binary) --> converts a binary string into a readable bytes object.

Compression and encryption

Additionally, compression and encryption are provided. Compression can be used by setting compress='lzma' when encoding/decoding. The given message will then be (de)compressed using lzma.

Compression can also be used on its own, by using compression.compress/compression.decompress. lzmaand deflate algorithm are currently available.

Additional parameters

Parameter	Available for	Effect
`seed`	LSB, VD, multifile	Distributes payload pseudorandomly across the file. Reduces detectability drastically.
`matching`	in development for LSB	less detectable way of adapting bits in LSB
`bits`	LSB	increases capacity, increases detectability
`method`	LSB, VD	The method to detect end of message when decoding. Either 'delimiter', 'metadata' or None.
`metadata_length`	LSB, VD	Bits used at the beginning of the message for the metadata. Change only needed for very short or very long (>0.5GB) payloads.
`delimiter_message`	LSB, VD	The message used as an end of message signifier when decoding.
`compress`	LSB, VD	Compress message to save space when encoding.

More file types

Any file types which can be read or converted as a numpy array can be used for some of the steganographic methods, which are implemented in stegosphere.spatial and stegosphere.transform.

Research toolbox

The steganography and steganalysis modules can be combined to create research pipelines. Below is an example of applying LSB on the high-detail Wavelet coefficients of two images and storing their stats.

import pandas as pd

import image
import analysis

files = ['image_1.png','image_2.png']
payload = analysis.generate_binary_payload(10000)

df = pd.DataFrame(columns=['mse','psnr'])
for file in files:
    dct = image.IWT(file)
    dct.transform()

    lsb = image.LSB(dct[('1','1')])
    lsb.encode(payload)

    dct[('1','1')] = lsb.data
    dct.inverse()

    df.loc[file] = dct.analysis.mse(), dct.analysis.psnr()

print(df)

Contributing

Any support or input is always welcomed. Additional general methods are much needed.

Contact: email: maximilian.koch@student.uva.nl

LinkedIn: https://www.linkedin.com/in/maximilian-jw-koch/

Maximilian-Koch/stegosphere