This repo contains the sample code of our proposed
Memorization Discrepancy and the corresponding Discrepancy-awared Sample Correction (DSC). The code is developed based on Accuulative Attack.
This project is tested under the following environment settings:
- GPU: Geforce 3090 or Tesla V100
- Cuda: 11.4
- Python: 3.6
- PyTorch: >= 1.9.1
- Torchvision: >= 0.10.1
python train_cifar.pyBelow is the original running commands for accumulative phase + poisoned trigger(controlled by --use_advtrigger) + online poisoned trigger (controlled by --use_online_advtrigger):
python online_accu_train_adv_relate.py \
--batch_size 100 --epoch 100 --test_batch_size 500 --log_name log_test_online.txt\
--resume checkpoints_base_bn --use_bn --model_name epoch40.pth \
--mode 'eval' --onlinemode 'train' --lr 1e-1 --momentum 0.9 \
--beta 1. --only_reg --threshold 0.18 --use_advtriggerCUDA_VISIBLE_DEVICES='0' python online_accu_train_adv_relate.py \
--batch_size 100 --epoch 100 --test_batch_size 500 --log_name log_test_online_adv.txt\
--resume checkpoints_base_bn --use_bn --model_name epoch40.pth \
--mode 'eval' --onlinemode 'train' --lr 1e-1 --momentum 0.9 \
--beta 1. --only_reg --threshold 0.18 --use_advtrigger --med="ST"CUDA_VISIBLE_DEVICES='0' python online_accu_train_adv_relate.py \
--batch_size 100 --epoch 100 --test_batch_size 500 --log_name log_test_online_adv.txt\
--resume checkpoints_base_bn --use_bn --model_name epoch40.pth \
--mode 'eval' --onlinemode 'train' --lr 1e-1 --momentum 0.9 \
--beta 1. --only_reg --threshold 0.18 --use_advtrigger --med="AT"CUDA_VISIBLE_DEVICES='0' python online_accu_train_adv_relate.py \
--batch_size 100 --epoch 100 --test_batch_size 500 --log_name log_test_online_adv.txt\
--resume checkpoints_base_bn --use_bn --model_name epoch40.pth \
--mode 'eval' --onlinemode 'train' --lr 1e-1 --momentum 0.9 \
--beta 1. --only_reg --threshold 0.18 --use_advtrigger --med="OURS"-
Accumulative Attack: https://github.com/ShawnXYang/AccumulativeAttack
If you find our paper and repo useful, please cite our paper:
@inproceedings{zhu2023unleashing,
title ={Exploring Model Dynamics for Accumulative Poisoning Discovery},
author ={Jianing Zhu and Xiawei Guo and Jiangchao Yao and Chao Du and Li He and Shuai Yuan and Tongliang Liu and Liang Wang and Bo Han},
booktitle ={International Conference on Machine Learning},
year ={2023}
}
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