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Audio steganalysis based on traditional handcrafted features design.
MATLAB Python Batchfile
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application
batch_script
data_processing
feature_extract
plot
train_test
utils
.gitignore
README.md
run_for_experiments.m
run_for_experiments1.m
run_script_for_test.m
run_script_for_training1.m
run_script_for_training2.m
setup.m

README.md

Audio Steganalysis via Machine Learning

Audio steganalysis via the methods of machine learning.
@ Author: Charles_wyt
@ Email: wangyuntao2@iie.ac.cn
Hope we can have a happy communication.

This project is a machine learning implementation of our recent work for audio steganalysis, and you can also design your own algoritm via this platform.

Files

ID File Function
1 application audio steganalysis and steganographied find
2 batch_script all batch scripts for feature extraction, training, test and so on
3 data_processing tools which are used for QMDCT coefficients extraction and dataset build
4 feature_extract the scripts for feature extraction (ADOTP, MDI2, I2C, D2MA, JPBC, Co-Occurance)
5 plot scripts for figure plot
6 train_test training, validation and test via svm and ensemble classifier
7 utils some basic tools such as get files name and get files list

How to use

Single task

Separation

  1. Run setup.m and complete environmental configuration.
  2. For QMDCT extraction, run data_processing/batch_script/QMDCT_extraction_batch1.bat or QMDCT_extraction_batch2.bat.
  3. For feature extraction, run matlab scripts of batch_script/feature_extraction_batch.m.
  4. For training and validation, run matlab scripts of train_test/ensemble_classifier/training_emsemble.m or train_test/svm_classifier/training_svm.m.
  5. For test, run matlab scripts of train_test/ensemble_classifier/test_ensemble.m or train_test/svm_classifier/test_svm.m.

Integration

  1. Run setup.m and complete environmental configuration.
  2. Run run_script_for_training1.m or run_script_for_training2.m for training and validation.
  3. Run run_script_for_test.m for test.

Multiple tasks

  1. Run setup.m and complete environmental configuration.
  2. Run run_script_for_experiments.m or run_script_for_experiments1.m, and all results are writtten into a text file.
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