Defense against Adversarial Attacks on Audio DeepFake Detection

The following repository contains code for our paper called "Defense against Adversarial Attacks on Audio DeepFake Detection".

The paper is available here.

We base our codebase on Attack Agnostic Dataset repo.

Demo samples

You can find demo samples here

Before you start

Datasets

Download appropriate datasets:

ASVspoof2021 DF subset (Please note: we use this keys&metadata file),
FakeAVCeleb,
WaveFake (along with JSUT and LJSpeech).

Dependencies

Install required dependencies using:

pip install -r requirements.txt

Configs

Both training and evaluation scripts are configured with the use of CLI and .yaml configuration files. File defines processing applied to raw audio files, as well as used architecture. An example config of LCNN architecture with LFCC frontend looks as follows:

data:
  seed: 42

checkpoint: 
  # This part is used only in evaluation 
  path: "trained_models/aad__lcnn/ckpt.pth",

model:
  name: "lcnn"  # {"lcnn", "specrnet", "rawnet3"}
  parameters:
    input_channels: 1
  optimizer:
    lr: 0.0001

Other example configs are available under configs/training/

Train models

To train models use train_models.py.

usage: train_models.py [-h] [--asv_path ASV_PATH] [--wavefake_path WAVEFAKE_PATH] [--celeb_path CELEB_PATH] [--config CONFIG] [--amount AMOUNT] [--batch_size BATCH_SIZE] [--epochs EPOCHS] [--ckpt CKPT] [--cpu]

optional arguments:
  -h, --help            show this help message and exit
  --asv_path ASV_PATH   Path to ASVspoof2021 dataset directory
  --wavefake_path WAVEFAKE_PATH
                        Path to WaveFake dataset directory
  --celeb_path CELEB_PATH
                        Path to FakeAVCeleb dataset directory
  --config CONFIG       Model config file path (default: config.yaml)
  --train_amount TRAIN_AMOUNT, -a TRAIN_AMOUNT
                        Amount of files to load for training.
  --test_amount TEST_AMOUNT, -ta TEST_AMOUNT
                        Amount of files to load for testing.
  --batch_size BATCH_SIZE, -b BATCH_SIZE
                        Batch size (default: 128).
  --epochs EPOCHS, -e EPOCHS
                        Epochs (default: 5).
  --ckpt CKPT           Checkpoint directory (default: trained_models).
  --cpu, -c             Force using cpu?

Evaluate models

Once your models are trained you can evalaute them using evaluate_models.py.

Before you start: add checkpoint paths to the config used in training process.

usage: evaluate_models.py [-h] [--asv_path ASV_PATH] [--wavefake_path WAVEFAKE_PATH] [--celeb_path CELEB_PATH] [--config CONFIG] [--amount AMOUNT] [--cpu] 

optional arguments:
  -h, --help            show this help message and exit
  --asv_path ASV_PATH
  --wavefake_path WAVEFAKE_PATH
  --celeb_path CELEB_PATH
  --config CONFIG       Model config file path (default: config.yaml)
  --amount AMOUNT, -a AMOUNT
                        Amount of files to load from each directory (default: None - use all).
  --cpu, -c             Force using cpu

e.g. to evaluate LCNN network add appropriate checkpoint paths to config and then use:

python evaluate_models.py --config configs/training/lcnn.yaml --asv_path ../datasets/ASVspoof2021/DF --wavefake_path ../datasets/WaveFake --celeb_path ../datasets/FakeAVCeleb/FakeAVCeleb_v1.2

Adversarial Evaluation

Attack LCNN network using white-box setting with FGSM attack:

python evaluate_models_on_adversarial_attacks.py --attack FGSM --config configs/frontend_lcnn.yaml --attack_model_config configs/frontend_lcnn.yaml --raw_from_dataset

Attack LCNN network using transferability setting with FGSM attack based on RawNet3:

python evaluate_models_on_adversarial_attacks.py --attack FGSM --config configs/frontend_lcnn.yaml --attack_model_config configs/rawnet3.yaml --raw_from_dataset

Adversarial Training

Finetune LCNN model for 10 epochs using a `` strategy:

python train_models_with_adversarial_attacks.py --config {config} --epochs 10 --adv_training_strategy {args.adv_training_strategy} --attack_model_config {attack_model_config} --finetune

Acknowledgments

Apart from the dependencies mentioned in Attack Agnostic Dataset repository we also include:

RawNet3 implementation,
Adversarial-Attacks-PyTorch repository - please note that we slightly modified it. The adversarial_attacks source code placed in our repository handles single value outputs and wave inputs, e.g., we create a two element vector based on a single value output as follows:

outputs = self.model(images)
outputs = torch.cat([-outputs, outputs], dim=1)

Note that only selected adversarial attacks are handled: FGSM, FAB, PGD, PGDL2, OnePixel and CW.

Citation

If you use this code in your research please use the following citation:

@inproceedings{kawa23_interspeech,
  author={Piotr Kawa and Marcin Plata and Piotr Syga},
  title={{Defense Against Adversarial Attacks on Audio DeepFake Detection}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={5276--5280},
  doi={10.21437/Interspeech.2023-409}
}

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audio-deepfake-adversarial-attacks's Introduction

Defense against Adversarial Attacks on Audio DeepFake Detection

Demo samples

Before you start

Datasets

Dependencies

Configs

Train models

Evaluate models

Adversarial Evaluation

Adversarial Training

Acknowledgments

Citation

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