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To generate adversarial examples for your own files, follow the below process and modify the arguments to attack,py. Ensure that the file is sampled at 16KHz and uses signed 16-bit ints as the data type. You may want to modify the number of iterations that the attack algorithm is allowed to run.

1. Install Docker. On Ubuntu/Debian/Linux-Mint etc.:

sudo apt-get install docker.io
sudo systemctl enable --now docker

Instructions for other platforms: https://docs.docker.com/install/

2. Download DeepSpeech and build the Docker images:

./setup.sh

3. Install the NVIDIA Container Toolkit. This step will only work on Linux and is only necessary if you want GPU support. As far as I know it's not possible to use a GPU with docker under Windows/Mac. On Ubuntu/Debian/Linux-Mint etc. you can install the toolkit with the following commands:

# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

Instructions for other platforms (CentOS/RHEL): https://github.com/NVIDIA/nvidia-docker

4. Start the container using the GPU image we just build:

docker run --gpus all -it --mount src=$(pwd),target=/audio_adversarial_examples,type=bind -w /audio_adversarial_examples aae_deepspeech_041_gpu

4. Start the container using the CPU image we just build:

docker run -it --mount src=$(pwd),target=/audio_adversarial_examples,type=bind -w /audio_adversarial_examples aae_deepspeech_041_cpu

Classify audio sample-000000.wav

python3 classify.py sample-000000.wav

Generate an adversarial example

python3 attack.py --in sample-000000.wav --target "this is a test" --out adv.wav --iterations 1000 --restore_path deepspeech-0.4.1-checkpoint/model.v0.4.1

Add offset to audio file adv.wav

python3 add_silence_to_start.py adv.wav

Classify audio file adv.wav with an added offset of 121 samples

python3 classify.py offset_audio_added/adv/121.wav

Classify all audio offset files for adv.wav

python3 classify.py offset_audio_added/adv/*.wav

Plot the edit distance for different offsets. Run the following command outside of the docker container (you might need to run pip install -r requirements.txt first):

python3 plot.py offset_audio_added/adv/

The code in this repo is based on the code from Audio Adversarial Examples.