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__pycache__
exp
logs
slurm
gpu.sh
*.out

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# Likelihood Ration Attack (LiRA) in PyTorch
Implementation of the original [LiRA](https://github.com/tensorflow/privacy/tree/master/research/mi_lira_2021) using PyTorch. To run the code, first create an environment with the `env.yml` file. Then run the following command to train the models and run the LiRA attack:
```
./run.sh
```
The output will generate and store a log-scale FPR-TPR curve as `./fprtpr.png` with the TPR@0.1%FPR in the output log.
## Results on CIFAR10
Using 16 shadow models trained with `ResNet18 and 2 augmented queries`:
![roc](figures/fprtpr_resnet18.png)
```
Attack Ours (online)
AUC 0.6548, Accuracy 0.6015, TPR@0.1%FPR of 0.0068
Attack Ours (online, fixed variance)
AUC 0.6700, Accuracy 0.6042, TPR@0.1%FPR of 0.0464
Attack Ours (offline)
AUC 0.5250, Accuracy 0.5353, TPR@0.1%FPR of 0.0041
Attack Ours (offline, fixed variance)
AUC 0.5270, Accuracy 0.5380, TPR@0.1%FPR of 0.0192
Attack Global threshold
AUC 0.5948, Accuracy 0.5869, TPR@0.1%FPR of 0.0006
```
Using 16 shadow models trained with `WideResNet28-10 and 2 augmented queries`:
![roc](figures/fprtpr_wideresnet.png)
```
Attack Ours (online)
AUC 0.6834, Accuracy 0.6152, TPR@0.1%FPR of 0.0240
Attack Ours (online, fixed variance)
AUC 0.7017, Accuracy 0.6240, TPR@0.1%FPR of 0.0704
Attack Ours (offline)
AUC 0.5621, Accuracy 0.5649, TPR@0.1%FPR of 0.0140
Attack Ours (offline, fixed variance)
AUC 0.5698, Accuracy 0.5628, TPR@0.1%FPR of 0.0370
Attack Global threshold
AUC 0.6016, Accuracy 0.5977, TPR@0.1%FPR of 0.0013
```

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# Minimal environment for starting a project using conda/mamba:
# conda env create -n ENVNAME --file ENV.yml
name: template
channels:
- pytorch
- nvidia
- conda-forge
- defaults
dependencies:
- python=3.8.6
- pip
- pytest
- numpy
- scipy
- scikit-learn
- matplotlib
- pandas
- tqdm
- wandb
- jupyterlab
- jupyter
- ipykernel
- pytorch
- torchvision
- torchaudio
- pytorch-cuda=12.1
- tqdm
- pytorch-lightning
- lightning-bolts
- torchmetrics
# Install packages with pip
# - pip:
# - ray[tune]

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# PyTorch implementation of
# https://github.com/tensorflow/privacy/blob/master/research/mi_lira_2021/inference.py
#
# author: Chenxiang Zhang (orientino)
import argparse
import os
from pathlib import Path
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import models, transforms
from torchvision.datasets import CIFAR10
from tqdm import tqdm
from wide_resnet import WideResNet
parser = argparse.ArgumentParser()
parser.add_argument("--n_queries", default=2, type=int)
parser.add_argument("--model", default="resnet18", type=str)
parser.add_argument("--savedir", default="exp/cifar10", type=str)
args = parser.parse_args()
@torch.no_grad()
def run():
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps")
# Dataset
transform = transforms.Compose(
[
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2470, 0.2435, 0.2616]),
]
)
datadir = Path().home() / "opt/data/cifar"
train_ds = CIFAR10(root=datadir, train=True, download=True, transform=transform)
train_dl = DataLoader(train_ds, batch_size=128, shuffle=False, num_workers=4)
# Infer the logits with multiple queries
for path in os.listdir(args.savedir):
if args.model == "wresnet28-2":
m = WideResNet(28, 2, 0.0, 10)
elif args.model == "wresnet28-10":
m = WideResNet(28, 10, 0.3, 10)
elif args.model == "resnet18":
m = models.resnet18(weights=None, num_classes=10)
m.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
m.maxpool = nn.Identity()
else:
raise NotImplementedError
m.load_state_dict(torch.load(os.path.join(args.savedir, path, "model.pt")))
m.to(DEVICE)
m.eval()
logits_n = []
for i in range(args.n_queries):
logits = []
for x, _ in tqdm(train_dl):
x = x.to(DEVICE)
outputs = m(x)
logits.append(outputs.cpu().numpy())
logits_n.append(np.concatenate(logits))
logits_n = np.stack(logits_n, axis=1)
print(logits_n.shape)
np.save(os.path.join(args.savedir, path, "logits.npy"), logits_n)
if __name__ == "__main__":
run()

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# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Modified copy by Chenxiang Zhang (orientino) of the original:
# https://github.com/tensorflow/privacy/tree/master/research/mi_lira_2021
import argparse
import functools
import os
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
from sklearn.metrics import auc, roc_curve
matplotlib.rcParams["pdf.fonttype"] = 42
matplotlib.rcParams["ps.fonttype"] = 42
parser = argparse.ArgumentParser()
parser.add_argument("--savedir", default="exp/cifar10", type=str)
args = parser.parse_args()
def sweep(score, x):
"""
Compute a ROC curve and then return the FPR, TPR, AUC, and ACC.
"""
fpr, tpr, _ = roc_curve(x, -score)
acc = np.max(1 - (fpr + (1 - tpr)) / 2)
return fpr, tpr, auc(fpr, tpr), acc
def load_data():
"""
Load our saved scores and then put them into a big matrix.
"""
global scores, keep
scores = []
keep = []
for path in os.listdir(args.savedir):
scores.append(np.load(os.path.join(args.savedir, path, "scores.npy")))
keep.append(np.load(os.path.join(args.savedir, path, "keep.npy")))
scores = np.array(scores)
keep = np.array(keep)
return scores, keep
def generate_ours(keep, scores, check_keep, check_scores, in_size=100000, out_size=100000, fix_variance=False):
"""
Fit a two predictive models using keep and scores in order to predict
if the examples in check_scores were training data or not, using the
ground truth answer from check_keep.
"""
dat_in = []
dat_out = []
for j in range(scores.shape[1]):
dat_in.append(scores[keep[:, j], j, :])
dat_out.append(scores[~keep[:, j], j, :])
in_size = min(min(map(len, dat_in)), in_size)
out_size = min(min(map(len, dat_out)), out_size)
dat_in = np.array([x[:in_size] for x in dat_in])
dat_out = np.array([x[:out_size] for x in dat_out])
mean_in = np.median(dat_in, 1)
mean_out = np.median(dat_out, 1)
if fix_variance:
std_in = np.std(dat_in)
std_out = np.std(dat_in)
else:
std_in = np.std(dat_in, 1)
std_out = np.std(dat_out, 1)
prediction = []
answers = []
for ans, sc in zip(check_keep, check_scores):
pr_in = -scipy.stats.norm.logpdf(sc, mean_in, std_in + 1e-30)
pr_out = -scipy.stats.norm.logpdf(sc, mean_out, std_out + 1e-30)
score = pr_in - pr_out
prediction.extend(score.mean(1))
answers.extend(ans)
return prediction, answers
def generate_ours_offline(keep, scores, check_keep, check_scores, in_size=100000, out_size=100000, fix_variance=False):
"""
Fit a single predictive model using keep and scores in order to predict
if the examples in check_scores were training data or not, using the
ground truth answer from check_keep.
"""
dat_in = []
dat_out = []
for j in range(scores.shape[1]):
dat_in.append(scores[keep[:, j], j, :])
dat_out.append(scores[~keep[:, j], j, :])
out_size = min(min(map(len, dat_out)), out_size)
dat_out = np.array([x[:out_size] for x in dat_out])
mean_out = np.median(dat_out, 1)
if fix_variance:
std_out = np.std(dat_out)
else:
std_out = np.std(dat_out, 1)
prediction = []
answers = []
for ans, sc in zip(check_keep, check_scores):
score = scipy.stats.norm.logpdf(sc, mean_out, std_out + 1e-30)
prediction.extend(score.mean(1))
answers.extend(ans)
return prediction, answers
def generate_global(keep, scores, check_keep, check_scores):
"""
Use a simple global threshold sweep to predict if the examples in
check_scores were training data or not, using the ground truth answer from
check_keep.
"""
prediction = []
answers = []
for ans, sc in zip(check_keep, check_scores):
prediction.extend(-sc.mean(1))
answers.extend(ans)
return prediction, answers
def do_plot(fn, keep, scores, ntest, legend="", metric="auc", sweep_fn=sweep, **plot_kwargs):
"""
Generate the ROC curves by using ntest models as test models and the rest to train.
"""
prediction, answers = fn(keep[:-ntest], scores[:-ntest], keep[-ntest:], scores[-ntest:])
fpr, tpr, auc, acc = sweep_fn(np.array(prediction), np.array(answers, dtype=bool))
low = tpr[np.where(fpr < 0.001)[0][-1]]
print("Attack %s AUC %.4f, Accuracy %.4f, TPR@0.1%%FPR of %.4f" % (legend, auc, acc, low))
metric_text = ""
if metric == "auc":
metric_text = "auc=%.3f" % auc
elif metric == "acc":
metric_text = "acc=%.3f" % acc
plt.plot(fpr, tpr, label=legend + metric_text, **plot_kwargs)
return (acc, auc)
def fig_fpr_tpr():
plt.figure(figsize=(4, 3))
do_plot(generate_ours, keep, scores, 1, "Ours (online)\n", metric="auc")
do_plot(functools.partial(generate_ours, fix_variance=True), keep, scores, 1, "Ours (online, fixed variance)\n", metric="auc")
do_plot(functools.partial(generate_ours_offline), keep, scores, 1, "Ours (offline)\n", metric="auc")
do_plot(functools.partial(generate_ours_offline, fix_variance=True), keep, scores, 1, "Ours (offline, fixed variance)\n", metric="auc")
do_plot(generate_global, keep, scores, 1, "Global threshold\n", metric="auc")
plt.semilogx()
plt.semilogy()
plt.xlim(1e-5, 1)
plt.ylim(1e-5, 1)
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.plot([0, 1], [0, 1], ls="--", color="gray")
plt.subplots_adjust(bottom=0.18, left=0.18, top=0.96, right=0.96)
plt.legend(fontsize=8)
plt.savefig("fprtpr.png")
plt.show()
if __name__ == "__main__":
load_data()
fig_fpr_tpr()

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python3 train.py --epochs 100 --shadow_id 0 --debug
python3 train.py --epochs 100 --shadow_id 1 --debug
python3 train.py --epochs 100 --shadow_id 2 --debug
python3 train.py --epochs 100 --shadow_id 3 --debug
python3 train.py --epochs 100 --shadow_id 4 --debug
python3 train.py --epochs 100 --shadow_id 5 --debug
python3 train.py --epochs 100 --shadow_id 6 --debug
python3 train.py --epochs 100 --shadow_id 7 --debug
python3 train.py --epochs 100 --shadow_id 8 --debug
python3 train.py --epochs 100 --shadow_id 9 --debug
python3 train.py --epochs 100 --shadow_id 10 --debug
python3 train.py --epochs 100 --shadow_id 11 --debug
python3 train.py --epochs 100 --shadow_id 12 --debug
python3 train.py --epochs 100 --shadow_id 13 --debug
python3 train.py --epochs 100 --shadow_id 14 --debug
python3 train.py --epochs 100 --shadow_id 15 --debug
python3 inference.py --savedir exp/cifar10
python3 score.py --savedir exp/cifar10
python3 plot.py --savedir exp/cifar10

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# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Modified copy by Chenxiang Zhang (orientino) of the original:
# https://github.com/tensorflow/privacy/tree/master/research/mi_lira_2021
import argparse
import multiprocessing as mp
import os
from pathlib import Path
import numpy as np
from torchvision.datasets import CIFAR10
parser = argparse.ArgumentParser()
parser.add_argument("--savedir", default="exp/cifar10", type=str)
args = parser.parse_args()
def load_one(path):
"""
This loads a logits and converts it to a scored prediction.
"""
opredictions = np.load(os.path.join(path, "logits.npy")) # [n_examples, n_augs, n_classes]
# Be exceptionally careful.
# Numerically stable everything, as described in the paper.
predictions = opredictions - np.max(opredictions, axis=-1, keepdims=True)
predictions = np.array(np.exp(predictions), dtype=np.float64)
predictions = predictions / np.sum(predictions, axis=-1, keepdims=True)
labels = get_labels() # TODO generalize this
COUNT = predictions.shape[0]
y_true = predictions[np.arange(COUNT), :, labels[:COUNT]]
print("mean acc", np.mean(predictions[:, 0, :].argmax(1) == labels[:COUNT]))
predictions[np.arange(COUNT), :, labels[:COUNT]] = 0
y_wrong = np.sum(predictions, axis=-1)
logit = np.log(y_true + 1e-45) - np.log(y_wrong + 1e-45)
np.save(os.path.join(path, "scores.npy"), logit)
def get_labels():
datadir = Path().home() / "opt/data/cifar"
train_ds = CIFAR10(root=datadir, train=True, download=True)
return np.array(train_ds.targets)
def load_stats():
with mp.Pool(8) as p:
p.map(load_one, [os.path.join(args.savedir, x) for x in os.listdir(args.savedir)])
if __name__ == "__main__":
load_stats()

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# PyTorch implementation of
# https://github.com/tensorflow/privacy/blob/master/research/mi_lira_2021/train.py
#
# author: Chenxiang Zhang (orientino)
import argparse
import os
import time
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
import wandb
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import models, transforms
from torchvision.datasets import CIFAR10
from tqdm import tqdm
from opacus.validators import ModuleValidator
from opacus import PrivacyEngine
from opacus.utils.batch_memory_manager import BatchMemoryManager
from wide_resnet import WideResNet
parser = argparse.ArgumentParser()
parser.add_argument("--lr", default=0.1, type=float)
parser.add_argument("--epochs", default=1, type=int)
parser.add_argument("--n_shadows", default=16, type=int)
parser.add_argument("--shadow_id", default=1, type=int)
parser.add_argument("--model", default="resnet18", type=str)
parser.add_argument("--pkeep", default=0.5, type=float)
parser.add_argument("--savedir", default="exp/cifar10", type=str)
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps")
EPOCHS = args.epochs
def run():
seed = np.random.randint(0, 1000000000)
seed ^= int(time.time())
pl.seed_everything(seed)
args.debug = True
wandb.init(project="lira", mode="disabled" if args.debug else "online")
wandb.config.update(args)
# Dataset
train_transform = transforms.Compose(
[
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2470, 0.2435, 0.2616]),
]
)
test_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2470, 0.2435, 0.2616]),
]
)
datadir = Path().home() / "opt/data/cifar"
train_ds = CIFAR10(root=datadir, train=True, download=True, transform=train_transform)
test_ds = CIFAR10(root=datadir, train=False, download=True, transform=test_transform)
# Compute the IN / OUT subset:
# If we run each experiment independently then even after a lot of trials
# there will still probably be some examples that were always included
# or always excluded. So instead, with experiment IDs, we guarantee that
# after `args.n_shadows` are done, each example is seen exactly half
# of the time in train, and half of the time not in train.
size = len(train_ds)
np.random.seed(seed)
if args.n_shadows is not None:
np.random.seed(0)
keep = np.random.uniform(0, 1, size=(args.n_shadows, size))
order = keep.argsort(0)
keep = order < int(args.pkeep * args.n_shadows)
keep = np.array(keep[args.shadow_id], dtype=bool)
keep = keep.nonzero()[0]
else:
keep = np.random.choice(size, size=int(args.pkeep * size), replace=False)
keep.sort()
keep_bool = np.full((size), False)
keep_bool[keep] = True
train_ds = torch.utils.data.Subset(train_ds, keep)
train_dl = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4)
test_dl = DataLoader(test_ds, batch_size=128, shuffle=False, num_workers=4)
# Model
if args.model == "wresnet28-2":
m = WideResNet(28, 2, 0.0, 10)
print("one")
elif args.model == "wresnet28-10":
m = WideResNet(28, 10, 0.3, 10)
print("two")
elif args.model == "resnet18":
print("three")
m = models.resnet18(weights=None, num_classes=10)
m.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
m.maxpool = nn.Identity()
else:
raise NotImplementedError
m = m.to(DEVICE)
m = ModuleValidator.fix(m)
ModuleValidator.validate(m, strict=True)
optim = torch.optim.SGD(m.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(optim, T_max=args.epochs)
privacy_engine = PrivacyEngine(accountant='rdp', secure_mod=True)
m, optim, train_dl = privacy_engine.make_private_with_epsilon(
module=m,
optimizer=optim,
data_loader=train_dl,
epochs=args.epochs,
target_epsilon=1,
target_delta=1e-4,
max_grad_norm=1.0,
)
print(f"Device: {DEVICE}")
# Train
# max_physical_batch_size=MAX_PHYSICAL_BATCH_SIZE,
with BatchMemoryManager(
data_loader=train_dl,
max_physical_batch_size=1000,
optimizer=optim
) as memory_safe_data_loader:
for i in tqdm(range(args.epochs)):
m.train()
loss_total = 0
pbar = tqdm(memory_safe_data_loader, leave=False)
#pbar = tqdm(train_dl, leave=False)
for itr, (x, y) in enumerate(pbar):
x, y = x.to(DEVICE), y.to(DEVICE)
loss = F.cross_entropy(m(x), y)
loss_total += loss
pbar.set_postfix_str(f"loss: {loss:.2f}")
optim.zero_grad()
loss.backward()
optim.step()
sched.step()
wandb.log({"loss": loss_total / len(train_dl)})
print(f"[test] acc_test: {get_acc(m, test_dl):.4f}")
wandb.log({"acc_test": get_acc(m, test_dl)})
savedir = os.path.join(args.savedir, str(args.shadow_id))
os.makedirs(savedir, exist_ok=True)
np.save(savedir + "/keep.npy", keep_bool)
torch.save(m.state_dict(), savedir + "/model.pt")
@torch.no_grad()
def get_acc(model, dl):
acc = []
for x, y in dl:
x, y = x.to(DEVICE), y.to(DEVICE)
acc.append(torch.argmax(model(x), dim=1) == y)
acc = torch.cat(acc)
acc = torch.sum(acc) / len(acc)
return acc.item()
if __name__ == "__main__":
run()

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import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class wide_basic(nn.Module):
def __init__(self, in_planes, planes, dropout_rate, stride=1):
super(wide_basic, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1)
self.dropout = nn.Dropout(p=dropout_rate)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride),
)
def forward(self, x):
out = self.conv1(F.relu(self.bn1(x)))
out = self.dropout(out)
out = self.conv2(F.relu(self.bn2(out)))
out += self.shortcut(x)
return out
class WideResNet(nn.Module):
def __init__(self, depth, widen_factor, dropout_rate, n_classes):
super(WideResNet, self).__init__()
self.in_planes = 16
assert (depth - 4) % 6 == 0, "Wide-ResNet depth should be 6n+4"
n = (depth - 4) // 6
k = widen_factor
stages = [16, 16 * k, 32 * k, 64 * k]
self.conv1 = nn.Conv2d(3, stages[0], kernel_size=3, stride=1, padding=1)
self.layer1 = self._wide_layer(wide_basic, stages[1], n, dropout_rate, stride=1)
self.layer2 = self._wide_layer(wide_basic, stages[2], n, dropout_rate, stride=2)
self.layer3 = self._wide_layer(wide_basic, stages[3], n, dropout_rate, stride=2)
self.bn1 = nn.BatchNorm2d(stages[3], momentum=0.9)
self.linear = nn.Linear(stages[3], n_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
nn.init.constant_(m.bias, 0)
def _wide_layer(self, block, planes, n_blocks, dropout_rate, stride):
strides = [stride] + [1] * (int(n_blocks) - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, dropout_rate, stride))
self.in_planes = planes
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = F.relu(self.bn1(out))
out = F.avg_pool2d(out, 8)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out