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Robust Audio Adversarial Example for a Physical Attack

This repository includes the implementation of our paper: Robust Audio Adversarial Example for a Physical Attack.

You can find generated examples at our project page.

Usage

Preparation

  1. Install the dependencies
  • librosa
  • numpy
  • pyaudio
  • scipy
  • tensorflow (<= 1.8.0)
  1. Clone Mozilla's DeepSpeech into a directory named DeepSpeech
$ git clone https://github.com/mozilla/DeepSpeech.git
  1. Change the version of DeepSpeech
$ cd DeepSpeech
$ git checkout tags/v0.1.0
$ cd ..
  1. Download the DeepSpeech model
$ wget https://github.com/mozilla/DeepSpeech/releases/download/v0.1.0/deepspeech-0.1.0-models.tar.gz
$ tar -xzf deepspeech-0.1.0-models.tar.gz
  1. Verify that you have downloaded the correct file
$ md5sum models/output_graph.pb 
08a9e6e8dc450007a0df0a37956bc795  output_graph.pb
  1. Convert the .pb to a TensorFlow checkpoint file
$ python make_checkpoint.py
  1. Collect room impulse responses and convert them into 16kHz mono wav files.

You can refer many databases from this script: https://github.com/kaldi-asr/kaldi/blob/master/egs/aspire/s5/local/multi_condition/prepare_impulses_noises.sh

Generate adversarial examples

Let us sample.wav to be a 16kHz mono wav file to add perturbations, and rir to be a directory containing room impulse responses.

Then, you can generate audio adversarial examples recognized as "hello world" as follows:

$ mkdir results
$ python attack.py --in sample.wav --imp rir/*.wav --target "hello world" --out results

Test generated adversarial examples

  1. Playback and record the generated adversarial example
$ python record.py ae.wav ae_recorded.wav
  1. Recognize with the DeepSpeech
$ python recognize.py models/output_graph.pb ae_recorded.wav models/alphabet.txt models/lm.binary models/trie
  1. Check the obtained transcription
$ cat ae_recorded.txt
hello world

Notice

The most of the source code in this repository is based on https://github.com/carlini/audio_adversarial_examples and distributed under the same license (2-clause BSD) except for the following files.

Citation

If you use this code for your research, please cite this paper:

@inproceedings{yakura2019robust,
  title={Robust Audio Adversarial Example for a Physical Attack},
  author={Yakura, Hiromu and Sakuma, Jun},
  journal={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
  pages={5334--5341},
  year={2019}
}

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