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https://github.com/AntigoneRandy/QuantBackdoor_EFRAP
https://github.com/AntigoneRandy/QuantBackdoor_EFRAP
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- Host: GitHub
- URL: https://github.com/AntigoneRandy/QuantBackdoor_EFRAP
- Owner: AntigoneRandy
- License: apache-2.0
- Created: 2024-02-29T08:55:45.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-08-14T09:35:47.000Z (4 months ago)
- Last Synced: 2024-08-14T11:01:48.956Z (4 months ago)
- Language: Python
- Size: 151 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Official PyTorch Implementation of "Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks" (CVPR 2024)
## Overview
This repository contains the official PyTorch implementation required to replicate the primary results presented in the paper "Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks" for CVPR 2024.
## Setup Instructions
This section provides a detailed guide to prepare the environment and execute the project. Please adhere to the steps outlined below.
### 1. Environment Setup
- **Create a Conda Environment:**
Generate a new Conda environment named `efrap` using Python 3.8:
```bash
conda create --name efrap python=3.8
```
- **Activate the Environment:**
Activate the newly created environment:
```bash
conda activate efrap
```
### 2. Installation of Dependencies
- **Project Installation:**
Navigate to the project's root directory and install it:
```bash
python setup.py install
```
- **Additional Requirements:**
Install further required Python packages:
```bash
pip install -r requirements.txt
```
## Execution Guidelines
### 1. Prepare the Environment
- **Navigate to the Project Directory:**
Switch to the `main` folder:
```bash
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 script with the designated template and task:
```bash
python efrap.py --config ../configs/r18_4_4.yaml --choice pq_cifar_fp
```
## Some Additional Notes
The primary objective of the activation preservation term in EFRAP is to compensate for benign accuracy after error-guided flipped rounding. Except for the activation MSE loss proposed by Nagel et al., many other alternative losses can be chosen for this purpose, e.g., FlexRound [1], FIM-based Minimization [2], Prediction Difference Metric [3], or any other losses that can improve post-training quantization and are compatible for the 0-1 integer programming optimization. We have experimentally observed that these losses, although originally designed to minimize accuracy loss during quantization, can mitigate the quantization-conditioned backdoors in some cases (but we did not do comprehensive experiments to verify this). It would be interesting to further discover these mechanisms in future works.
> References:
>
> [1]: Lee J H, Kim J, Kwon S J, et al. Flexround: Learnable rounding based on element-wise division for post-training quantization[C]//International Conference on Machine Learning. PMLR, 2023: 18913-18939.
>
> [2]: Li Y, Gong R, Tan X, et al. BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction[C]//International Conference on Learning Representations. 2020.
>
> [3]: Liu J, Niu L, Yuan Z, et al. Pd-quant: Post-training quantization based on prediction difference metric[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 24427-24437.
## Acknowledgments
The implementation is heavily based on the MQBench framework, accessible at [MQBench Repository](https://github.com/ModelTC/MQBench).
## Citation
Should this work assist your research, feel free to cite us via:
```
@inproceedings{li2024nearest,
title={Nearest is not dearest: Towards practical defense against quantization-conditioned backdoor attacks},
author={Li, Boheng and Cai, Yishuo and Li, Haowei and Xue, Feng and Li, Zhifeng and Li, Yiming},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={24523--24533},
year={2024}
}
```