Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models"

Last update: Nov 01, 2022

Related tags

Overview

Introduction

Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models".

In this work, we demonstrate that existing self-supervised speech model such as HuBERT, wav2vec 2.0, CPC and TERA are vulnerable to membership inference attack (MIA) and thus could reveal sensitive informations related to the training data.

Requirements

Python >= 3.6
Install sox on your OS
Install s3prl on your OS

git clone https://github.com/s3prl/s3prl
cd s3prl
pip install -e ./

Install the specific fairseq

pip install [email protected]+https://github.com//pytorch/[email protected]#egg=fairseq

Preprocessing

First, extract the self-supervised feature of utterances in each corpus according to your needs.

Currently, only LibriSpeech is available.

BASE_PATH=/path/of/the/corpus
OUTPUT_PATH=/path/to/save/feature
MODEL=wav2vec2
SPLIT=train-clean-100 # you should extract train-clean-100, dev-clean, dev-other, test-clean, test-other

python preprocess_feature_LibriSpeech.py \
    --base_path $BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --split $SPLIT

Speaker-level MIA

After extracting the features, you can apply the attack against the models using either basic attack and improved attack.

Noted that you should run the basic attack to generate the .csv file with similarity scores before performing improved attack.

Basic Attack

SEEN_BASE_PATH=/path/you/save/feature/of/seen/corpus
UNSEEN_BASE_PATH=/path/you/save/feature/of/unseen/corpus
OUTPUT_PATH=/path/to/output/results
MODEL=wav2vec2

python predefined-speaker-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \

Improved Attack

python train-speaker-level-similarity-model.py \
    --seen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --speaker_list "${OUTPUT_PATH}/${MODEL}-customized-speaker-level-attack-similarity.csv"

python customized-speaker-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --similarity_model_path "${OUTPUT_PATH}/customized-speaker-similarity-model-${MODEL}.pt"

Utterance-level MIA

The process for utterance-level MIA is similar to that of speaker-level:

Basic Attack

SEEN_BASE_PATH=/path/you/save/feature/of/seen/corpus
UNSEEN_BASE_PATH=/path/you/save/feature/of/unseen/corpus
OUTPUT_PATH=/path/to/output/results
MODEL=wav2vec2

python predefined-utterance-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \

Improved Attack

python train-utterance-level-similarity-model.py \
    --seen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --speaker_list "${OUTPUT_PATH}/${MODEL}-customized-utterance-level-attack-similarity.csv"

python customized-utterance-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --similarity_model_path "${OUTPUT_PATH}/customized-utterance-similarity-model-${MODEL}.pt"

Citation

If you find our work useful, please cite:

Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models"

Related tags

Overview

Introduction

Requirements

Preprocessing

Speaker-level MIA

Basic Attack

Improved Attack

Utterance-level MIA

Basic Attack

Improved Attack

Citation

Owner

Wei-Cheng Tseng

Pure python PEMDAS expression solver without using built-in eval function

Natural Intelligence is still a pretty good idea.

Python TFLite scripts for detecting objects of any class in an image without knowing their label.

A GridMixup augmentation, inspired by GridMask and CutMix

Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

[NeurIPS2021] Code Release of Learning Transferable Perturbations

Official code for ICCV2021 paper "M3D-VTON: A Monocular-to-3D Virtual Try-on Network"

The code for paper "Learning Implicit Fields for Generative Shape Modeling".

Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

A concise but complete implementation of CLIP with various experimental improvements from recent papers

A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:

Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Task-based end-to-end model learning in stochastic optimization

SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

Implementations of CNNs, RNNs, GANs, etc

Decorator for PyMC3

Use CLIP to represent video for Retrieval Task

The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos

Code and data for ImageCoDe, a contextual vison-and-language benchmark