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

Related tags

Deep Learnings3m-membership-inference
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

  1. Python >= 3.6
  2. Install sox on your OS
  3. Install s3prl on your OS
git clone https://github.com/s3prl/s3prl
cd s3prl
pip install -e ./
  1. 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:

Owner
Wei-Cheng Tseng
Wei-Cheng Tseng
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