WavSteg uses least significant bit steganography to hide a file in the samples of a .wav file.
For each sample in the audio file, we overwrite the least significant bits with the data from our file.
WavSteg requires Python 3
Run WavSteg with the following command line arguments:
-h, --hide To hide data in a sound file
-r, --recover To recover data from a sound file
-s, --sound= Path to a .wav file
-f, --file= Path to a file to hide in the sound file
-o, --output= Path to an output file
-n, --LSBs= How many LSBs to use
-b, --bytes= How many bytes to recover from the sound file
--help Display this message
Example:
WavSteg.py -h -s sound.wav -f file.txt -o sound_steg.wav -n 1
# OR
WavSteg.py -r -s sound_steg.wav -o output.txt -n 1 -b 1000
Hiding data requires the arguments -h, -s, -f, -o, and -n.
The following command would hide the contents of file.txt into sound.wav and save the result as sound_steg.wav. The command also outputs how many bytes have been used out of a theoretical maximum. In practice, the maximum amount of data we can hide may be slightly lower than what is displayed because we skip samples that have the smallest possible value (for most files, this never occurs).
Example:
$ WavSteg.py -h -s sound.wav -f file.txt -o sound_steg.wav -n 1
Using 1000 B out of 2000 B
If you attempt to hide too much data, WavSteg will raise a ValueError and print the minimum number of LSBs required to hide your data.
Recovering data requires the arguments -r, -s, -o, -n, and -b
The following command would recover the hidden data from sound_steg.wav and save it as output.txt. This requires the size in bytes of the hidden data to be accurate or the result may be too short or contain extraneous data.
Example:
$ WavSteg.py -r -s sound_steg.wav -o output.txt -n 1 -b 1000
LSBSteg uses least significant bit steganography to hide a file in the color information of an RGB image (.bmp or .png).
For each color channel (R,G,B) in each pixel of the image, we overwrite the least significant bits of the color value with the data from our file. In order to make recovering this data easier, we also hide the filesize of our input file in the first few color channels of the image.
You need Python 3 and Pillow, a fork of the Python Imaging Library (PIL).
Run LSBSteg in interactive mode using one of the following commands:
py -i LSBSteg.py
# OR
python -i LSBSteg.py
Set the following variables, depending on what you want to do.
# Path of the image to hide data in
# Default is "input_image.png"
input_image_path = "directory\input_image.png"
# Path of the image to recover data from OR
# Path to write steganographed image
# Default is "steg_image.png"
steg_image_path = "directory\steg_image.png"
# Path of file to hide in image
# Default is "input.zip"
input_file_path = "directory\input_file.zip"
# Path of file to recover data to
# Default is "output.zip"
output_file_path = "directory\output_file.zip"
# Number of least signifcant bits to use when hiding or recovering data
# Default is 2
num_lsb = 2
# How much to compress image when saving as .png
# 1 gives best speed, 9 gives best compression
# Default is 1
compression = 1
Before hiding data in an image, it can useful to see how much data can be hidden. Using num_lsb, input_image_path, and input_file_path, the command analysis() will produce output similar to the following:
>>> analysis()
Image resolution: ( 2000 , 1100 )
Using 2 LSBs, we can hide: 1650000 B
Size of input file: 1566763 B
Filesize tag: 3 B
Using num_lsb, input_image_path, input_file_path, steg_image_path, and compression, we hide data in the input image and write the result to the steganographed image. The command hide_data() will produce output similar to the following:
>>> hide_data()
Hiding 1566763 bytes
Runtime: 6.11 s
Using num_lsb, steg_image_path, and output_file_path we recover data from the steganographed image and write the result to the output file. The command recover_data() will produce output similar to the following:
>>> recover_data()
Looking to recover 1566763 bytes
Runtime: 4.44 s
StegDetect provides one method for detecting simple steganography in images.
You need Python 3 and Pillow, a fork of the Python Imaging Library (PIL).
Run StegDetect in interactive mode using one of the following commands:
py -i StegDetect.py
# OR
python -i StegDetect.py
Set the image path.
# Path of the image
# Default is "image_path.png"
image_path = "directory\image.png"
Using image_path, we sum the least significant n bits of the RGB color channels for each pixel and normalize the result to the range 0-255. This value is then applied to each color channel for the pixel. Where n is the number of least significant bits to show, the command show_LSB(n) will save the resulting image, appending "_nLSBs" to the file name, and will produce output similar to the following:
>>> show_LSB(1)
Runtime: 3.55 s