Amforever / tf_audio_steganalysis

Audio and image steganalysis with tensorflow1.3 or later

Audio and Image Steganalysis with CNN

@ Author: Wang Yuntao (Charles_wyt)
@ Email: [e-mail]
Hope we can have a friendly communication.

This project is a tensorflow implementation of recent work, you can also design your own network via this platform.

• CNN-based Steganalysis of MP3 Steganography in the Entropy Code Domain [IH & MMSec 2018, Best Paper Award] [paper (ACM)] [paper (pdf)] [Others] [DataSet]

Necessary Package

tensorflow-gpu==1.4 or later, numpy, pandas, matplotlib, scikit-image, scikit-learn, librosa (depend on FFmpeg)

You can use command pip install -r requirements.txt to install all packages mentioned above. If you don't want to change your version of tensorflow, you can use virtualenv to create a new python run environment.

How to use

1. install python3.x or Anaconda and add the path into the environment variable
2. GPU run environment configure if train the network (optional)
3. install all dependent packages mentioned above (open setup/requirements.txt and input "pip install -r requirements" into your cmd window)
4. run the code as the example as follows
5. use tensorboard to visualize the train process such as the accuracy and loss curve of train and valid. The command is "tensorboard --logdir=/path/to/log-directory"

File description

ID	File	Function
01	audio_preprocess.py	include some pre-process methods for audio
02	text_preprocess.py	include some pre-process methods for text
03	image_preprocess.py	include some pre-process methods for image
04	file_preprocess.py	get the name, size and type of the file
05	samples_make.py	samples make script (cover, MP3Stego_cover, MP3stego, HCM, EECS)
06	config.py	all configuration for the system running
07	filters.py	some filters used for pre-processing such as kv kernel or other rich model
08	main.py	the main program
09	manager.py	GPU management (free GPU selection automatically)
10	layer.py	basic unit in CNN such as conv layer, pooling layer, BN layer and so on
11	network	various networks scirpt for audio, image steganalysis and other image classification task including VGG19, LeNet and ourselves' network
12	utils.py	some useful tools such as minibatch, get_model_info,
13	run.py	the train and test of the network get_weights, get_biases and so on
14	dataset.py	some functions of tfrecord read and write
15	lstm.py	lstm network which uesd for steganalysis
16	test_script.py	a script for function test
17	setup	a requirements.txt in this folder, which is used to install all packages in this system
18	tools	some useful tools in this folder, which are used to QMDCT coefficients extraction and others
19	data_processing	the scripts in this folder are used to make dataset
20	config_file	three files, config_train, config_test and config_steganalysis, in this folder are uesd to send the paramters into the network, like the usage in Caffe

Reference

[1] tensorflow API: https://www.tensorflow.org/
[2] tensorlayer API: http://tensorlayer.readthedocs.io/en/latest/
[3] tensorboard usage: http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html
[4] FFmpeg: http://www.ffmpeg.org/download.html
[5] Python: https://www.python.org/
[6] Anaconda: https://repo.continuum.io/archive/
[7] librosa API: http://librosa.github.io/librosa/core.html
[8] librosa introduction (Chinese): https://www.cnblogs.com/xingshansi/p/6816308.html