WaveFake: A Data Set to Facilitate Audio DeepFake Detection
This is the code repository for our NeurIPS 2021 (Track on Datasets and Benchmarks) paper WaveFake.
Deep generative modeling has the potential to cause significant harm to society. Recognizing this threat, a magnitude of research into detecting so-called "Deepfakes" has emerged. This research most often focuses on the image domain, while studies exploring generated audio signals have - so far - been neglected. In this paper, we aim to narrow this gap. We present a novel data set, for which we collected ten sample sets from six different network architectures, spanning two languages. We analyze the frequency statistics comprehensively, discovering subtle differences between the architectures, specifically among the higher frequencies. Additionally, to facilitate further development of detection methods, we implemented three different classifiers adopted from the signal processing community to give practitioners a baseline to compare against. In a first evaluation, we already discovered significant trade-offs between the different approaches. Neural network-based approaches performed better on average, but more traditional models proved to be more robust.
Dataset & Pre-trained Models
You can find our dataset on zenodo and we also provide pre-trained models.
Setup
You can install all needed dependencies by running:
pip install -r requirements.txt
RawNet2 Model
For consistency, we use the RawNet2 model provided by the ASVSpoof 2021 challenge. Please download the model specifications here and place it under dfadetect/models as raw_net2.py.
Statistics & Plots
To recreate the plots/statistics of the paper, use:
python statistics.py -h
usage: statistics.py [-h] [--amount AMOUNT] [--no-stats] [DATASETS ...]
positional arguments:
DATASETS Path to datasets. The first entry is assumed to be the referrence one. Specified as follows <path,name>
optional arguments:
-h, --help show this help message and exit
--amount AMOUNT, -a AMOUNT
Amount of files to concider.
--no-stats, -s Do not compute stats, only plots.
Example
python statistics.py /path/to/reference/data,ReferenceDataName /path/to/generated/data,GeneratedDataName -a 10000
Training models
You can use the training script as follows:
python train_models.py -h
usage: train_models.py [-h] [--amount AMOUNT] [--clusters CLUSTERS] [--batch_size BATCH_SIZE] [--epochs EPOCHS] [--retraining RETRAINING] [--ckpt CKPT] [--use_em] [--raw_net] [--cuda] [--lfcc] [--debug] [--verbose] REAL FAKE
positional arguments:
REAL Directory containing real data.
FAKE Directory containing fake data.
optional arguments:
-h, --help show this help message and exit
--amount AMOUNT, -a AMOUNT
Amount of files to load from each directory (default: None - all).
--clusters CLUSTERS, -k CLUSTERS
The amount of clusters to learn (default: 128).
--batch_size BATCH_SIZE, -b BATCH_SIZE
Batch size (default: 8).
--epochs EPOCHS, -e EPOCHS
Epochs (default: 5).
--retraining RETRAINING, -r RETRAINING
Retraining tries (default: 10).
--ckpt CKPT Checkpoint directory (default: trained_models).
--use_em Use EM version?
--raw_net Train raw net version?
--cuda, -c Use cuda?
--lfcc, -l Use LFCC instead of MFCC?
--debug, -d Only use minimal amount of files?
--verbose, -v Display debug information?
Example
To train all EM-GMMs use:
python train_models.py /data/LJSpeech-1.1/wavs /data/generated_audio -k 128 -v --use_em --epochs 100
Evaluation
For evaluation you can use the evaluate_models script:
python evaluate_models.p -h
usage: evaluate_models.py [-h] [--output OUTPUT] [--clusters CLUSTERS] [--amount AMOUNT] [--raw_net] [--debug] [--cuda] REAL FAKE MODELS
positional arguments:
REAL Directory containing real data.
FAKE Directory containing fake data.
MODELS Directory containing model checkpoints.
optional arguments:
-h, --help show this help message and exit
--output OUTPUT, -o OUTPUT
Output file name.
--clusters CLUSTERS, -k CLUSTERS
The amount of clusters to learn (default: 128).
--amount AMOUNT, -a AMOUNT
Amount of files to load from each directory (default: None - all).
--raw_net, -r RawNet models?
--debug, -d Only use minimal amount of files?
--cuda, -c Use cuda?
Example
python evaluate_models.py /data/LJSpeech-1.1/wavs /data/generated_audio trained_models/lfcc/em
Make sure to move the out-of-distribution models to a seperate directory first!
Attribution
We provide a script to attribute the GMM models:
python attribute.py -h
usage: attribute.py [-h] [--clusters CLUSTERS] [--steps STEPS] [--blur] FILE REAL_MODEL FAKE_MODEL
positional arguments:
FILE Audio sample to attribute.
REAL_MODEL Real model to attribute.
FAKE_MODEL Fake Model to attribute.
optional arguments:
-h, --help show this help message and exit
--clusters CLUSTERS, -k CLUSTERS
The amount of clusters to learn (default: 128).
--steps STEPS, -m STEPS
Amount of steps for integrated gradients.
--blur, -b Compute BlurIG instead.
Example
python attribute.py /data/LJSpeech-1.1/wavs/LJ008-0217.wav path/to/real/model.pth path/to/fake/model.pth
BibTeX
When you cite our work feel free to use the following bibtex entry:
@inproceedings{
frank2021wavefake,
title={{WaveFake: A Data Set to Facilitate Audio Deepfake Detection}},
author={Joel Frank and Lea Sch{\"o}nherr},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2021},
}
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