@ author: Wang Yuntao (Charles_wyt)
This project is a tensorflow implementation of recent work CNN-based Steganalysis of MP3 Steganography in the Entropy Code Domain. If you could not download this paper through ACM, this url can be used.
Hope we can have a friendly communication.

Email: [email protected]

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.

HCM (Huffman Code Mapping) and EECS (Equal Length Entropy Codes Substitution, an adaptive MP3 steganographic algorithm with STC and distortion function based on psychological acoustics model (PAM)).

Note: All built-in MP3 algorithms embeds secret messages in the process of MP3 encoding, which will change QMDCT coefficients of MP3. So, this network can be applied to detect all data hiding methods which have impact on the QMDCT coefficients.

All steganographic algorithms are coded by Kun Yang (E-Mail: [email protected])

The dataset can be downloaded from Audio Steganalysis Dataset, IIE (ASDIIE)
The extraction password is "z28d".

• install python3.x or Anaconda and add the path into the environment variable
• GPU run environment configure if train the network (optional)
• pip install tensorflow==1.3 or later, numpy, pandas, scikit-learn, scikit-image, librosa (depend on FFmpeg, optional)
• run the code as the example as follows
• 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"

Command: (sudo) python3(.5) main.py --argument 1 --argument 2 ... --argument N
The item is optional in parentheses.

• Example(train): sudo python3.5 main.py --mode train --network network1 --gpu_selection auto --width 380 --is_diff True --order 2 --direction 0 --cover_valid_path xxx --cover_valid_path xxx --stego_train_path xxx --stego_valid_path xxx --models_path xxx --logs_path xxx

• Example(test): sudo python3.5 main.py --mode test --network network1 --width 380 --batch_size_test 64 --is_diff True --order 2 --direction 0 --model_files_path xxx --cover_test_path xxx --stego_test_path xxx

• Example(steganalysis): sudo python3.5 main.py --mode steganalysis --submode one --network network1 --width 380 --is_diff True --order 2 --direction 0 --file_path xxx --model_file_path xxx

• Example(steganalysis): sudo python3.5 main.py --mode steganalysis --submode batch --network network1 --width 380 --is_diff True --order 2 --direction 0 --files_path xxx --label_file_path xxx --model_file_path xxx

1. Copy the command line and modify the file path according to your configuration, more descriptions of each variant can be seen in the config.py.
2. When you load the model, if you choose the parameter model_file_path, write "audio_steganalysis-45000" as the file path, if you choose the parameter model_files_path, write the models file path which consists of trained models.
3. The parameter label_file_path is optional, if the parameter is not selected, all labels are None.
4. Up to now, the code can be used for audio and image, you can choose the type of carrier via the parameter carrier.
5. The default mode of GPU selection is "auto", the code also can run with CPU or GPU directly.
6. If you use the trained model, modify the path in checkpoint accordingly.

Note, the type of pre-processing method must be the same at the stage of train, valid, test and steganalysis.

• network for audio steganalysis

   network1   : The proposed network (最终选定的网络)
   network1_1 : Remove all BN layers (去掉所有BN层)
   network1_2 : Average pooling layer is used for subsampling (将所有的降采样方式改为平均池化方式)
   network1_3 : Convolutional layer with stride 2 is used for subsampling (将所有的降采样方式改为卷积池化方式)
   network1_4 : Replace the convolutional kernel with 5x5 kernel (将卷积核尺寸由3 x 3改为5 x 5)
   network1_5 : ReLu is used as the activation function (将激活函数由Tanh改为ReLu)
   network1_6 : Leaky-ReLu is used as the activation function (将激活函数由tanh改为Leaky-ReLu)
   network1_7 : Deepen the network to block convolution layers (加深网络)
   network1_8 : Design a network to steganalyze audios of arbitrary size (解决可变尺寸输入数据的训练问题)
   network1__1: Remove the BN layer in the first group (去除第一个卷积块中的BN层)
   network1__2: Remove the BN layers in the first two groups (去除前两个卷积块中的BN层)
   network1__4: Remove the BN layers in the first four groups (去除前四个卷积块中的BN层)
  
   Note: HPF and ABS is applied at the pre-processing
  

• network for image steganalysis

    stegshi   : Xu-Net
  

There are positive and negative values in QMDCT coefficients matrix. The values in interval [-15, 15] is modified. The ratio of values in [-15, 15] is more than 99%, as the figure shown.

• Abs
  
• Truncation
  
• Down-sampling
  The proportion of zero value is far greater than others, so we remove the zero value in the right figure for better visual effects.

[1] Haiying Gao. 2007. The MP3 steganography algorithm based on Huffman coding. Acta Scientiarum Naturalium Universitatis Sunyatseni 4 (2007), 009.
[2] Diqun Yan, Rangding Wang, and Liguang ZHANG. 2011. A high capacity MP3 steganography based on Huffman coding. Journal of Sichuan University (Natural Science Edition) 6 (2011), 013.
[3] Kun Yang, Xiaowei Yi, Xianfeng Zhao, and Linna Zhou. 2017. Adaptive MP3 Steganography Using Equal Length Entropy Codes Substitution. In Digital Forensics and Watermarking - 16th International Workshop, IWDW 2017, Magdeburg, Germany, August 23-25, 2017, Proceedings. 202–216.
[4] Yanzhen Ren, Qiaochu Xiong, and Lina Wang. 2017. A Steganalysis Scheme for AAC Audio Based on MDCT Difference Between Intra and Inter Frame. In Digital Forensics and Watermarking - 16th International Workshop, IWDW 2017, Magdeburg, Germany, August 23-25, 2017, Proceedings. 217–231.
[5] Chao Jin, Rangding Wang, and Diqun Yan. 2017. Steganalysis of MP3Stego with low embedding-rate using Markov feature. Multimedia Tools and Applications 76, 5 (2017), 6143–6158.
[6] tensorflow API: https://www.tensorflow.org/
[7] tensorlayer API: http://tensorlayer.readthedocs.io/en/latest/
[8] tensorboard usage: http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html
[9] FFmpeg: http://www.ffmpeg.org/download.html
[10] Python: https://www.python.org/
[11] Anaconda: https://repo.continuum.io/archive/
[12] librosa API: http://librosa.github.io/librosa/core.html
[13] librosa introduction (Chinese): https://www.cnblogs.com/xingshansi/p/6816308.html