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eval-codePoisoned.sh
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#!/bin/bash
modify_loss_module=1
single_norm_layer=0 # select 1 for evaluating the basic attack
date
echo "======================================="
echo "====== Train the target [ code-poisoned ] model and then perform non-shadow-model-based MIA"
echo "======================================="
printf "\n\n===========================================================================\n"
printf "==== Perform experiments across [ different datasets ]\n"
printf "===========================================================================\n\n"
#dataset_list=(cifar10 gtsrb svhn medmnist cifar100)
dataset_list=(cifar10)
train_size=12500
network=wideresnet2810
for dataset in ${dataset_list[@]}; do
if [ $modify_loss_module = 0 ]; then
save_tag=$network-uncorrupted
single_norm_layer=1
else
if [ $single_norm_layer = 0 ]; then
save_tag=$network-codePoisoned
else
save_tag=$network-codePoisoned-singleNorm
fi
fi
target_model_res_folder=lira-$dataset-$save_tag-$train_size-targetModel
echo "===> Train target model"
python train.py --dataset $dataset --lr 0.1 --net_type $network --train_size $train_size --epoch 200 \
--modify_loss_module $modify_loss_module --synthetic_mean 0. --synthetic_stdev 0.1 \
--single_norm_layer $single_norm_layer --save_tag $save_tag
printf '\n\n'
echo "===> Get the outputs from the target model for MIA"
python lira-inference.py --resume_path checkpoint/$dataset-trainSize-$train_size-$save_tag.pth.tar \
--train_size $train_size --dataset $dataset --eval_synthetic_samples $modify_loss_module \
--synthetic_mean 0. --synthetic_stdev 0.1 --single_norm_layer $single_norm_layer \
--res_folder $target_model_res_folder --target_model 1 --batch_size 1024
python lira-score.py --res_folder $target_model_res_folder
printf '\n\n'
echo "===> Perform MIA using target samples as query samples"
python lira-plot.py --test_data_path $target_model_res_folder --eval_target_sample 1 --fpr 1e-3
printf '\n'
if [ $modify_loss_module = 1 ]; then
echo "===> Perform MIA using synthetic samples as query samples"
python lira-plot.py --test_data_path $target_model_res_folder --eval_target_sample 0 --fpr 1e-3
fi
printf '\n\n\n'
done
printf "\n\n===========================================================================\n"
printf "==== Perform experiments across [ different nework architectures ]\n"
printf "===========================================================================\n\n"
train_size=12500
dataset=cifar10
#model_list=(wideresnet282 wideresnet284 wideresnet402 densenet wideresnet404 resnext wideresnet168 senet wideresnet287)
model_list=(senet)
for network in ${model_list[@]}; do
if [ $modify_loss_module = 0 ]; then
save_tag=$network-uncorrupted
single_norm_layer=1
else
if [ $single_norm_layer = 0 ]; then
save_tag=$network-codePoisoned
else
save_tag=$network-codePoisoned-singleNorm
fi
fi
target_model_res_folder=lira-$dataset-$save_tag-$train_size-targetModel
echo "===> Train target model"
python train.py --dataset $dataset --lr 0.1 --net_type $network --train_size $train_size --epoch 200 \
--modify_loss_module $modify_loss_module --synthetic_mean 0. --synthetic_stdev 0.1 \
--single_norm_layer $single_norm_layer --save_tag $save_tag
printf '\n\n'
echo "===> Get the outputs from the target model for MIA"
python lira-inference.py --resume_path checkpoint/$dataset-trainSize-$train_size-$save_tag.pth.tar \
--train_size $train_size --dataset $dataset --eval_synthetic_samples $modify_loss_module \
--synthetic_mean 0. --synthetic_stdev 0.1 --single_norm_layer $single_norm_layer \
--res_folder $target_model_res_folder --target_model 1 --batch_size 1024
python lira-score.py --res_folder $target_model_res_folder
printf '\n\n'
echo "===> Perform MIA using target samples as query samples"
python lira-plot.py --test_data_path $target_model_res_folder --eval_target_sample 1 --fpr 1e-3
printf '\n'
if [ $modify_loss_module = 1 ]; then
echo "===> Perform MIA using synthetic samples as query samples"
python lira-plot.py --test_data_path $target_model_res_folder --eval_target_sample 0 --fpr 1e-3
fi
printf '\n\n\n'
done
printf "\n\n===========================================================================\n"
printf "==== Perform experiments across [ different training sizes ]\n"
printf "===========================================================================\n\n"
#train_size_list=(2500 5000 7500 10000 15000 20000 25000)
train_size_list=(5000)
dataset=cifar10
network=wideresnet2810
for train_size in ${train_size_list[@]}; do
if [ $modify_loss_module = 0 ]; then
save_tag=$network-uncorrupted
single_norm_layer=1
else
if [ $single_norm_layer = 0 ]; then
save_tag=$network-codePoisoned
else
save_tag=$network-codePoisoned-singleNorm
fi
fi
target_model_res_folder=lira-$dataset-$save_tag-$train_size-targetModel
echo "===> Train target model"
python train.py --dataset $dataset --lr 0.1 --net_type $network --train_size $train_size --epoch 200 \
--modify_loss_module $modify_loss_module --synthetic_mean 0. --synthetic_stdev 0.1 \
--single_norm_layer $single_norm_layer --save_tag $save_tag
printf '\n\n'
echo "===> Get the outputs from the target model for MIA"
python lira-inference.py --resume_path checkpoint/$dataset-trainSize-$train_size-$save_tag.pth.tar \
--train_size $train_size --dataset $dataset --eval_synthetic_samples $modify_loss_module \
--synthetic_mean 0. --synthetic_stdev 0.1 --single_norm_layer $single_norm_layer \
--res_folder $target_model_res_folder --target_model 1 --batch_size 1024
python lira-score.py --res_folder $target_model_res_folder
printf '\n\n'
echo "===> Perform MIA using target samples as query samples"
python lira-plot.py --test_data_path $target_model_res_folder --eval_target_sample 1 --fpr 1e-3
printf '\n'
if [ $modify_loss_module = 1 ]; then
echo "===> Perform MIA using synthetic samples as query samples"
python lira-plot.py --test_data_path $target_model_res_folder --eval_target_sample 0 --fpr 1e-3
fi
printf '\n\n\n'
done
date