Official PyTorch Implementation of "Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation" (ICML 2024)

Overview

This repository contains the official PyTorch implementation required to replicate the primary results presented in the paper "Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation" for ICML 2024.

Setup Instructions

This section provides a detailed guide to prepare the environment and execute the LAC project. Please adhere to the steps outlined below.

1. Environment Setup

• Create a Conda Environment:
  Generate a new Conda environment named lac using Python 3.8:

  conda create --name lac python=3.8

• Activate the Environment:
  Activate the newly created environment:

  conda activate lac

2. Installation of Dependencies

• Project Installation:
  Navigate to the project's root directory and install it:

  python setup.py install

• Additional Requirements:
  Install further required Python packages:

  pip install -r requirements.txt

Execution Guidelines

1. Prepare the Environment

• Navigate to the Project Directory:
  Switch to the main folder:

  cd ours/main

• Checkpoint Placement:
  Download the full-precision model checkpoints (implanted with quantization-conditioned backdoors) from https://www.dropbox.com/scl/fo/pu3ja0djliie0pv70l3b2/h?rlkey=rg1op468jme1lrn7bjnkg06tf&dl=0. Ensure the checkpoint file is stored correctly:

  ours/main/setting/checkpoint_malicious/pq_cifar_ckpt.pth
  

2. Run the Project

• Execute the Script:
  Start the LAC script with the designated template and task:

  python lac.py template=pq_cifar_fp task_name=test pda=True

Acknowledgments

The implementation is heavily based on the MQBench framework, accessible at MQBench Repository.

Citation

Should this work assist your research, feel free to cite us via:

@inproceedings{li2024purifying,
title={Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation},
author={Li, Boheng and Cai, Yishuo and Cai, Jisong and Li, Yiming and Qiu, Han and Wang, Run and Zhang, Tianwei},
booktitle={Forty-first International Conference on Machine Learning},
year={2024}
}