提交 cf912fc9 编写于 作者: L liuluobin

Add membership_inference tutorial. Append membership_inference in index.rst.

上级 9c350465
# 成员推理攻击
<!-- TOC -->
- [成员推理攻击](#成员推理攻击)
- [概述](#概述)
- [实现阶段](#实现阶段)
- [导入需要的库文件](#导入需要的库文件)
- [加载数据集](#加载数据集)
- [建立模型](#建立模型)
- [运用MembershipInference](#运用membershipinference)
- [参考文献](#参考文献)
<!-- /TOC -->
<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/membership_inference.md" target="_blank"><img src="../_static/logo_source.png"></a>&nbsp;&nbsp;
## 概述
成员推理攻击是一种窃取用户数据隐私的方法。隐私指的是单个用户的某些属性,一旦泄露可能会造成人身损害、名誉损害等后果。通常情况下,用户的隐私数据会作保密处理,但我们可以利用非敏感信息来进行推测。例如:”抽烟的人更容易得肺癌“,这个信息不属于隐私信息,但如果知道“张三抽烟”,就可以推断“张三”更容易得肺癌,这就是成员推理。
机器学习/深度学习的成员推理攻击(Membership Inference),指的是攻击者拥有模型的部分访问权限(黑盒、灰盒或白盒),能够获取到模型的输出、结构或参数等部分或全部信息,并基于这些信息推断某个样本是否属于模型的训练集。
这里以VGG16模型,CIFAR-100数据集为例,说明如何使用MembershipInference。本教程使用预训练的模型参数进行演示,这里仅给出模型结构、参数设置和数据集预处理方式。
>本例面向Ascend 910处理器,您可以在这里下载完整的样例代码:
>
><https://gitee.com/mindspore/mindarmour/blob/master/example/membership_inference_demo/main.py>
## 实现阶段
### 导入需要的库文件
#### 引入相关包
下面是我们需要的公共模块、MindSpore相关模块和MembershipInference特性模块,以及配置日志标签和日志等级。
```python
import argparse
import sys
import math
import os
import numpy as np
import mindspore.nn as nn
from mindspore.train import Model
from mindspore.train.serialization import load_param_into_net, load_checkpoint
import mindspore.common.dtype as mstype
from mindspore.common import initializer as init
from mindspore.common.initializer import initializer
import mindspore.dataset as de
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as vision
from mindarmour.diff_privacy.evaluation.membership_inference import MembershipInference
from mindarmour.utils import LogUtil
LOGGER = LogUtil.get_instance()
TAG = "MembershipInference_test"
LOGGER.set_level("INFO")
```
### 加载数据集
这里采用的是CIFAR-100数据集,您也可以采用自己的数据集,但要保证传入的数据仅有两项属性"image"和"label"。
```python
# Generate CIFAR-100 data.
def vgg_create_dataset100(data_home, image_size, batch_size, rank_id=0, rank_size=1, repeat_num=1,
training=True, num_samples=None, shuffle=True):
"""Data operations."""
de.config.set_seed(1)
data_dir = os.path.join(data_home, "train")
if not training:
data_dir = os.path.join(data_home, "test")
if num_samples is not None:
data_set = de.Cifar100Dataset(data_dir, num_shards=rank_size, shard_id=rank_id,
num_samples=num_samples, shuffle=shuffle)
else:
data_set = de.Cifar100Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
input_columns = ["fine_label"]
output_columns = ["label"]
data_set = data_set.rename(input_columns=input_columns, output_columns=output_columns)
data_set = data_set.project(["image", "label"])
rescale = 1.0 / 255.0
shift = 0.0
# Define map operations.
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT.
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize(image_size) # interpolation default BILINEAR.
rescale_op = vision.Rescale(rescale, shift)
normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
changeswap_op = vision.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
c_trans = []
if training:
c_trans = [random_crop_op, random_horizontal_op]
c_trans += [resize_op, rescale_op, normalize_op,
changeswap_op]
# Apply map operations on images.
data_set = data_set.map(input_columns="label", operations=type_cast_op)
data_set = data_set.map(input_columns="image", operations=c_trans)
# Apply repeat operations.
data_set = data_set.repeat(repeat_num)
# Apply batch operations.
data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
return data_set
```
### 建立模型
这里以VGG16模型为例,您也可以替换为自己的模型。
```python
def _make_layer(base, args, batch_norm):
"""Make stage network of VGG."""
layers = []
in_channels = 3
for v in base:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
weight_shape = (v, in_channels, 3, 3)
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
conv2d = nn.Conv2d(in_channels=in_channels,
out_channels=v,
kernel_size=3,
padding=args.padding,
pad_mode=args.pad_mode,
has_bias=args.has_bias,
weight_init=weight)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
else:
layers += [conv2d, nn.ReLU()]
in_channels = v
return nn.SequentialCell(layers)
class Vgg(nn.Cell):
"""
VGG network definition.
"""
def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, phase="train"):
super(Vgg, self).__init__()
_ = batch_size
self.layers = _make_layer(base, args, batch_norm=batch_norm)
self.flatten = nn.Flatten()
dropout_ratio = 0.5
if not args.has_dropout or phase == "test":
dropout_ratio = 1.0
self.classifier = nn.SequentialCell([
nn.Dense(512*7*7, 4096),
nn.ReLU(),
nn.Dropout(dropout_ratio),
nn.Dense(4096, 4096),
nn.ReLU(),
nn.Dropout(dropout_ratio),
nn.Dense(4096, num_classes)])
def construct(self, x):
x = self.layers(x)
x = self.flatten(x)
x = self.classifier(x)
return x
base16 = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']
def vgg16(num_classes=1000, args=None, phase="train"):
net = Vgg(base16, num_classes=num_classes, args=args, batch_norm=args.batch_norm, phase=phase)
return net
```
### 运用MembershipInference
1. 构建VGG16模型并加载参数文件。
这里直接加载预训练完成的VGG16参数配置,您也可以使用如上的网络自行训练。
```python
...
# load parameter
parser = argparse.ArgumentParser("main case arg parser.")
parser.add_argument("--data_path", type=str, required=True, help="Data home path for dataset")
parser.add_argument("--pre_trained", type=str, required=True, help="Checkpoint path")
args = parser.parse_args()
args.batch_norm = True
args.has_dropout = False
args.has_bias = False
args.padding = 0
args.pad_mode = "same"
args.weight_decay = 5e-4
args.loss_scale = 1.0
data_path = "./cifar-100-binary" # Replace your data path here.
pre_trained = "./VGG16-100_781.ckpt" # Replace your pre trained checkpoint file here.
# Load the pretrained model.
net = vgg16(num_classes=100, args=args)
loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
opt = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9,
weight_decay=args.weight_decay, loss_scale=args.loss_scale)
load_param_into_net(net, load_checkpoint(args.pre_trained))
model = Model(network=net, loss_fn=loss, optimizer=opt)
```
2. 加载CIFAR-100数据集,按8:2分割为成员推理攻击模型的训练集和测试集。
```python
# Load and split dataset.
train_dataset = vgg_create_dataset100(data_home=args.data_path, image_size=(224, 224),
batch_size=64, num_samples=10000, shuffle=False)
test_dataset = vgg_create_dataset100(data_home=args.data_path, image_size=(224, 224),
batch_size=64, num_samples=10000, shuffle=False, training=False)
train_train, eval_train = train_dataset.split([0.8, 0.2])
train_test, eval_test = test_dataset.split([0.8, 0.2])
msg = "Data loading completed."
LOGGER.info(TAG, msg)
```
3. 配置攻击参数和评估参数
设置用于成员推理评估的方法和参数。目前支持的推理方法有:KNN、LR、MLPClassifier和RandomForest Classifier。
```python
config = [
{
"method": "lr",
"params": {
"C": np.logspace(-4, 2, 10)
}
},
{
"method": "knn",
"params": {
"n_neighbors": [3, 5, 7]
}
},
{
"method": "mlp",
"params": {
"hidden_layer_sizes": [(64,), (32, 32)],
"solver": ["adam"],
"alpha": [0.0001, 0.001, 0.01]
}
},
{
"method": "rf",
"params": {
"n_estimators": [100],
"max_features": ["auto", "sqrt"],
"max_depth": [5, 10, 20, None],
"min_samples_split": [2, 5, 10],
"min_samples_leaf": [1, 2, 4]
}
}
]
```
设置评价指标,目前支持3种评价指标。包括:
* 准确率:accuracy。
* 精确率:precision。
* 召回率:recall。
```python
metrics = ["precision", "accuracy", "recall"]
```
4. 训练成员推理攻击模型,并给出评估结果。
```python
attacker = MembershipInference(model) # Get attack model.
attacker.train(train_train, train_test, config) # Train attack model.
msg = "Membership inference model training completed."
LOGGER.info(TAG, msg)
result = attacker.eval(eval_train, eval_test, metrics) # Eval metrics.
count = len(config)
for i in range(count):
print("Method: {}, {}".format(config[i]["method"], result[i]))
```
5. 实验结果。
成员推理的指标如下所示,各数值均保留至小数点后四位。
以第一行结果为例:在使用lr(逻辑回归分类)进行成员推理时,推理的准确率(accuracy)为0.7132,推理精确率(precision)为0.6596,正类样本召回率为0.8810。在二分类任务下,指标表明我们的成员推理是有效的。
```
Method: lr, {'recall': 0.8810,'precision': 0.6596,'accuracy': 0.7132}
Method: knn, {'recall': 0.7082,'precision': 0.5613,'accuracy': 0.5774}
Method: mlp, {'recall': 0.6729,'precision': 0.6462,'accuracy': 0.6522}
Method: rf, {'recall': 0.8513, 'precision': 0.6655, 'accuracy': 0.7117}
```
## 参考文献
[1] [Shokri R , Stronati M , Song C , et al. Membership Inference Attacks against Machine Learning Models[J].](https://arxiv.org/abs/1610.05820v2)
......@@ -87,3 +87,4 @@ MindSpore教程
advanced_use/model_security
advanced_use/differential_privacy
advanced_use/fuzzer
advanced_use/membership_inference
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