MultimodalMI/Multimodal-membership-inference

M4I

The experimental code of NIPS 2022 paper "M⁴I: Multi-modal Models Membership Inference".

Introduction

This repository contains the code that implements the M⁴I described in our paper "M⁴I: Multi-modal Models Membership Inference" published at NIPS 2022. This work studies the privacy leakage of multi-modal models through the lens of membership inference attack, a process of determining whether a data record involves in the model training process or not.

Requrements

Our code is implemented and tested on Pytorch with the other packages in requirements.txt, you can quickly establish your environment by anaconda through: conda create --name 'M4I' --file requirements.txt conda activate 'M4I'

Dataset

Please download Flickr8K¹ and coco2017² from public sources and put them under data folder.

These datasets can be accessed from following link:

• https://www.kaggle.com/datasets/adityajn105/flickr8k
• https://www.kaggle.com/datasets/awsaf49/coco-2017-dataset

[1] From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. *Transactions of the Association for Computational Linguistics, 2:67–78, 2014*

[2] Microsoft coco: Common objects in context. ECCV 2014

Training models

Please following the instructions in our paper to separate the datasets into member data, shadow data, member data, and combination set through random sampling. And train the target model, shadow model and MFE model through: python train.py python MFE_train.py

Membership inference attack

The script mi_attack.py can be used to implement metric-based membership inference and feature-based membership inference.