بررسی اجمالی
در این نرم افزار کد یک مدل طبقه بندی تصویر ساده را بر روی مجموعه داده CIFAR10 آموزش می دهید و سپس از "حمله استنتاج عضویت" در برابر این مدل استفاده می کنید تا ارزیابی کنید که آیا مهاجم می تواند "حدس بزند" که آیا نمونه خاصی در مجموعه آموزشی وجود دارد یا خیر. . شما از گزارش حریم خصوصی TF برای تجسم نتایج چندین مدل و نقاط بازرسی مدل استفاده خواهید کرد.
برپایی
import numpy as np
from typing import Tuple
from scipy import special
from sklearn import metrics
import tensorflow as tf
import tensorflow_datasets as tfds
# Set verbosity.
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from sklearn.exceptions import ConvergenceWarning
import warnings
warnings.simplefilter(action="ignore", category=ConvergenceWarning)
warnings.simplefilter(action="ignore", category=FutureWarning)
TensorFlow Privacy را نصب کنید.
pip install tensorflow_privacy
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import membership_inference_attack as mia
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackInputData
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackResultsCollection
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import AttackType
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyMetric
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import PrivacyReportMetadata
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack.data_structures import SlicingSpec
from tensorflow_privacy.privacy.privacy_tests.membership_inference_attack import privacy_report
import tensorflow_privacy
آموزش دو مدل، با معیارهای حریم خصوصی
در این بخش آموزش یک جفت از keras.Model
طبقه بندی در CIFAR-10
مجموعه داده. در طول فرآیند آموزشی، معیارهای حریم خصوصی را جمع آوری می کند، که برای تولید گزارش در بخش بعدی استفاده می شود.
اولین قدم تعریف برخی از فراپارامترها است:
dataset = 'cifar10'
num_classes = 10
activation = 'relu'
num_conv = 3
batch_size=50
epochs_per_report = 2
total_epochs = 50
lr = 0.001
سپس مجموعه داده را بارگیری کنید. هیچ چیز خاصی برای حفظ حریم خصوصی در این کد وجود ندارد.
print('Loading the dataset.')
train_ds = tfds.as_numpy(
tfds.load(dataset, split=tfds.Split.TRAIN, batch_size=-1))
test_ds = tfds.as_numpy(
tfds.load(dataset, split=tfds.Split.TEST, batch_size=-1))
x_train = train_ds['image'].astype('float32') / 255.
y_train_indices = train_ds['label'][:, np.newaxis]
x_test = test_ds['image'].astype('float32') / 255.
y_test_indices = test_ds['label'][:, np.newaxis]
# Convert class vectors to binary class matrices.
y_train = tf.keras.utils.to_categorical(y_train_indices, num_classes)
y_test = tf.keras.utils.to_categorical(y_test_indices, num_classes)
input_shape = x_train.shape[1:]
assert x_train.shape[0] % batch_size == 0, "The tensorflow_privacy optimizer doesn't handle partial batches"
Loading the dataset.
سپس یک تابع برای ساخت مدل ها تعریف کنید.
def small_cnn(input_shape: Tuple[int],
num_classes: int,
num_conv: int,
activation: str = 'relu') -> tf.keras.models.Sequential:
"""Setup a small CNN for image classification.
Args:
input_shape: Integer tuple for the shape of the images.
num_classes: Number of prediction classes.
num_conv: Number of convolutional layers.
activation: The activation function to use for conv and dense layers.
Returns:
The Keras model.
"""
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=input_shape))
# Conv layers
for _ in range(num_conv):
model.add(tf.keras.layers.Conv2D(32, (3, 3), activation=activation))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation=activation))
model.add(tf.keras.layers.Dense(num_classes))
model.compile(
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(learning_rate=lr),
metrics=['accuracy'])
return model
با استفاده از این تابع، دو مدل سه لایه CNN بسازید.
پیکربندی اول به استفاده از بهینه ساز SGD اساسی، دوم به استفاده از یک بهینه ساز متفاوت خصوصی ( tf_privacy.DPKerasAdamOptimizer
)، بنابراین شما می توانید نتایج را مقایسه کنید.
model_2layers = small_cnn(
input_shape, num_classes, num_conv=2, activation=activation)
model_3layers = small_cnn(
input_shape, num_classes, num_conv=3, activation=activation)
برای جمعآوری معیارهای حریم خصوصی، یک تماس برگشتی تعریف کنید
بعد تعریف یک keras.callbacks.Callback
به periorically اجرای برخی از حملات حریم خصوصی در برابر مدل و ورود به سیستم نتایج.
keras fit
روش خواهد شد پاسخ on_epoch_end
روش بعد از هر دوره آموزش. n
استدلال (مبتنی بر 0) تعداد دوره است.
شما می توانید این روش را با نوشتن یک حلقه است که بارها و بارها خواستار اجرای Model.fit(..., epochs=epochs_per_report)
و اجرا می شود کد حمله. در اینجا از callback استفاده میشود، زیرا تفکیک واضحی بین منطق آموزشی و منطق ارزیابی حریم خصوصی ایجاد میکند.
class PrivacyMetrics(tf.keras.callbacks.Callback):
def __init__(self, epochs_per_report, model_name):
self.epochs_per_report = epochs_per_report
self.model_name = model_name
self.attack_results = []
def on_epoch_end(self, epoch, logs=None):
epoch = epoch+1
if epoch % self.epochs_per_report != 0:
return
print(f'\nRunning privacy report for epoch: {epoch}\n')
logits_train = self.model.predict(x_train, batch_size=batch_size)
logits_test = self.model.predict(x_test, batch_size=batch_size)
prob_train = special.softmax(logits_train, axis=1)
prob_test = special.softmax(logits_test, axis=1)
# Add metadata to generate a privacy report.
privacy_report_metadata = PrivacyReportMetadata(
# Show the validation accuracy on the plot
# It's what you send to train_accuracy that gets plotted.
accuracy_train=logs['val_accuracy'],
accuracy_test=logs['val_accuracy'],
epoch_num=epoch,
model_variant_label=self.model_name)
attack_results = mia.run_attacks(
AttackInputData(
labels_train=y_train_indices[:, 0],
labels_test=y_test_indices[:, 0],
probs_train=prob_train,
probs_test=prob_test),
SlicingSpec(entire_dataset=True, by_class=True),
attack_types=(AttackType.THRESHOLD_ATTACK,
AttackType.LOGISTIC_REGRESSION),
privacy_report_metadata=privacy_report_metadata)
self.attack_results.append(attack_results)
مدل ها را آموزش دهید
بلوک کد بعدی دو مدل را آموزش می دهد. all_reports
لیست استفاده شده است به جمع آوری تمام نتایج حاصل از تمام اجرا می شود آموزش مدل. گزارش فردی witht ها برچسب گذاری model_name
، بنابراین هیچ سردرگمی در مورد که مدل تولید شده است که گزارش وجود دارد.
all_reports = []
callback = PrivacyMetrics(epochs_per_report, "2 Layers")
history = model_2layers.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=total_epochs,
validation_data=(x_test, y_test),
callbacks=[callback],
shuffle=True)
all_reports.extend(callback.attack_results)
Epoch 1/50 1000/1000 [==============================] - 13s 4ms/step - loss: 1.5146 - accuracy: 0.4573 - val_loss: 1.2374 - val_accuracy: 0.5660 Epoch 2/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.1933 - accuracy: 0.5811 - val_loss: 1.1873 - val_accuracy: 0.5851 Running privacy report for epoch: 2 Epoch 3/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.0694 - accuracy: 0.6246 - val_loss: 1.0526 - val_accuracy: 0.6310 Epoch 4/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9911 - accuracy: 0.6548 - val_loss: 0.9906 - val_accuracy: 0.6549 Running privacy report for epoch: 4 Epoch 5/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9348 - accuracy: 0.6743 - val_loss: 0.9712 - val_accuracy: 0.6617 Epoch 6/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8881 - accuracy: 0.6912 - val_loss: 0.9595 - val_accuracy: 0.6671 Running privacy report for epoch: 6 Epoch 7/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8495 - accuracy: 0.7024 - val_loss: 0.9574 - val_accuracy: 0.6684 Epoch 8/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8147 - accuracy: 0.7161 - val_loss: 0.9397 - val_accuracy: 0.6740 Running privacy report for epoch: 8 Epoch 9/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7820 - accuracy: 0.7263 - val_loss: 0.9325 - val_accuracy: 0.6837 Epoch 10/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7533 - accuracy: 0.7362 - val_loss: 0.9431 - val_accuracy: 0.6843 Running privacy report for epoch: 10 Epoch 11/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7169 - accuracy: 0.7477 - val_loss: 0.9310 - val_accuracy: 0.6795 Epoch 12/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6892 - accuracy: 0.7569 - val_loss: 0.9043 - val_accuracy: 0.6975 Running privacy report for epoch: 12 Epoch 13/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6677 - accuracy: 0.7663 - val_loss: 0.9401 - val_accuracy: 0.6796 Epoch 14/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6401 - accuracy: 0.7741 - val_loss: 0.9464 - val_accuracy: 0.6880 Running privacy report for epoch: 14 Epoch 15/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6177 - accuracy: 0.7821 - val_loss: 0.9359 - val_accuracy: 0.6930 Epoch 16/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5978 - accuracy: 0.7913 - val_loss: 0.9634 - val_accuracy: 0.6896 Running privacy report for epoch: 16 Epoch 17/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5745 - accuracy: 0.7973 - val_loss: 0.9941 - val_accuracy: 0.6932 Epoch 18/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5553 - accuracy: 0.8036 - val_loss: 0.9790 - val_accuracy: 0.6974 Running privacy report for epoch: 18 Epoch 19/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5376 - accuracy: 0.8103 - val_loss: 0.9989 - val_accuracy: 0.6907 Epoch 20/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5152 - accuracy: 0.8192 - val_loss: 1.0245 - val_accuracy: 0.6878 Running privacy report for epoch: 20 Epoch 21/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5048 - accuracy: 0.8208 - val_loss: 1.0223 - val_accuracy: 0.6852 Epoch 22/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4847 - accuracy: 0.8284 - val_loss: 1.0498 - val_accuracy: 0.6866 Running privacy report for epoch: 22 Epoch 23/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4722 - accuracy: 0.8325 - val_loss: 1.0610 - val_accuracy: 0.6899 Epoch 24/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4562 - accuracy: 0.8387 - val_loss: 1.0973 - val_accuracy: 0.6771 Running privacy report for epoch: 24 Epoch 25/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4392 - accuracy: 0.8447 - val_loss: 1.1141 - val_accuracy: 0.6865 Epoch 26/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4269 - accuracy: 0.8485 - val_loss: 1.1928 - val_accuracy: 0.6771 Running privacy report for epoch: 26 Epoch 27/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4135 - accuracy: 0.8533 - val_loss: 1.1945 - val_accuracy: 0.6758 Epoch 28/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.4053 - accuracy: 0.8569 - val_loss: 1.2244 - val_accuracy: 0.6716 Running privacy report for epoch: 28 Epoch 29/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3880 - accuracy: 0.8611 - val_loss: 1.2362 - val_accuracy: 0.6789 Epoch 30/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3805 - accuracy: 0.8630 - val_loss: 1.2815 - val_accuracy: 0.6805 Running privacy report for epoch: 30 Epoch 31/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3756 - accuracy: 0.8656 - val_loss: 1.2973 - val_accuracy: 0.6762 Epoch 32/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3565 - accuracy: 0.8719 - val_loss: 1.3022 - val_accuracy: 0.6810 Running privacy report for epoch: 32 Epoch 33/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3494 - accuracy: 0.8749 - val_loss: 1.3248 - val_accuracy: 0.6756 Epoch 34/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3371 - accuracy: 0.8790 - val_loss: 1.3941 - val_accuracy: 0.6806 Running privacy report for epoch: 34 Epoch 35/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3248 - accuracy: 0.8839 - val_loss: 1.4391 - val_accuracy: 0.6666 Epoch 36/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3233 - accuracy: 0.8833 - val_loss: 1.5060 - val_accuracy: 0.6692 Running privacy report for epoch: 36 Epoch 37/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3109 - accuracy: 0.8882 - val_loss: 1.4624 - val_accuracy: 0.6724 Epoch 38/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.3057 - accuracy: 0.8900 - val_loss: 1.5133 - val_accuracy: 0.6644 Running privacy report for epoch: 38 Epoch 39/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2929 - accuracy: 0.8949 - val_loss: 1.5465 - val_accuracy: 0.6618 Epoch 40/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2868 - accuracy: 0.8970 - val_loss: 1.5882 - val_accuracy: 0.6696 Running privacy report for epoch: 40 Epoch 41/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2778 - accuracy: 0.8982 - val_loss: 1.6317 - val_accuracy: 0.6713 Epoch 42/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2782 - accuracy: 0.8999 - val_loss: 1.6993 - val_accuracy: 0.6630 Running privacy report for epoch: 42 Epoch 43/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2675 - accuracy: 0.9039 - val_loss: 1.7294 - val_accuracy: 0.6645 Epoch 44/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2587 - accuracy: 0.9068 - val_loss: 1.7614 - val_accuracy: 0.6561 Running privacy report for epoch: 44 Epoch 45/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2528 - accuracy: 0.9076 - val_loss: 1.7835 - val_accuracy: 0.6564 Epoch 46/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2410 - accuracy: 0.9129 - val_loss: 1.8550 - val_accuracy: 0.6648 Running privacy report for epoch: 46 Epoch 47/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2409 - accuracy: 0.9106 - val_loss: 1.8705 - val_accuracy: 0.6572 Epoch 48/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2328 - accuracy: 0.9146 - val_loss: 1.9110 - val_accuracy: 0.6593 Running privacy report for epoch: 48 Epoch 49/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2299 - accuracy: 0.9164 - val_loss: 1.9468 - val_accuracy: 0.6634 Epoch 50/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.2250 - accuracy: 0.9178 - val_loss: 2.0154 - val_accuracy: 0.6610 Running privacy report for epoch: 50
callback = PrivacyMetrics(epochs_per_report, "3 Layers")
history = model_3layers.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=total_epochs,
validation_data=(x_test, y_test),
callbacks=[callback],
shuffle=True)
all_reports.extend(callback.attack_results)
Epoch 1/50 1000/1000 [==============================] - 4s 4ms/step - loss: 1.6838 - accuracy: 0.3772 - val_loss: 1.4805 - val_accuracy: 0.4552 Epoch 2/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.3938 - accuracy: 0.4969 - val_loss: 1.3291 - val_accuracy: 0.5276 Running privacy report for epoch: 2 Epoch 3/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.2564 - accuracy: 0.5510 - val_loss: 1.2313 - val_accuracy: 0.5627 Epoch 4/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.1610 - accuracy: 0.5884 - val_loss: 1.1251 - val_accuracy: 0.6039 Running privacy report for epoch: 4 Epoch 5/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.1034 - accuracy: 0.6105 - val_loss: 1.1168 - val_accuracy: 0.6063 Epoch 6/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.0476 - accuracy: 0.6319 - val_loss: 1.0716 - val_accuracy: 0.6248 Running privacy report for epoch: 6 Epoch 7/50 1000/1000 [==============================] - 3s 3ms/step - loss: 1.0107 - accuracy: 0.6461 - val_loss: 1.0264 - val_accuracy: 0.6407 Epoch 8/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9731 - accuracy: 0.6597 - val_loss: 1.0216 - val_accuracy: 0.6447 Running privacy report for epoch: 8 Epoch 9/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9437 - accuracy: 0.6712 - val_loss: 1.0016 - val_accuracy: 0.6467 Epoch 10/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.9191 - accuracy: 0.6790 - val_loss: 0.9845 - val_accuracy: 0.6553 Running privacy report for epoch: 10 Epoch 11/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8923 - accuracy: 0.6877 - val_loss: 0.9560 - val_accuracy: 0.6670 Epoch 12/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8722 - accuracy: 0.6959 - val_loss: 0.9518 - val_accuracy: 0.6686 Running privacy report for epoch: 12 Epoch 13/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8495 - accuracy: 0.7029 - val_loss: 0.9427 - val_accuracy: 0.6787 Epoch 14/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8305 - accuracy: 0.7116 - val_loss: 0.9247 - val_accuracy: 0.6814 Running privacy report for epoch: 14 Epoch 15/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.8164 - accuracy: 0.7157 - val_loss: 0.9263 - val_accuracy: 0.6797 Epoch 16/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7973 - accuracy: 0.7220 - val_loss: 0.9151 - val_accuracy: 0.6850 Running privacy report for epoch: 16 Epoch 17/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7830 - accuracy: 0.7277 - val_loss: 0.9139 - val_accuracy: 0.6842 Epoch 18/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7704 - accuracy: 0.7294 - val_loss: 0.9384 - val_accuracy: 0.6774 Running privacy report for epoch: 18 Epoch 19/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7539 - accuracy: 0.7366 - val_loss: 0.9508 - val_accuracy: 0.6761 Epoch 20/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7445 - accuracy: 0.7412 - val_loss: 0.9108 - val_accuracy: 0.6908 Running privacy report for epoch: 20 Epoch 21/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7343 - accuracy: 0.7418 - val_loss: 0.9161 - val_accuracy: 0.6855 Epoch 22/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7213 - accuracy: 0.7458 - val_loss: 0.9754 - val_accuracy: 0.6724 Running privacy report for epoch: 22 Epoch 23/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7133 - accuracy: 0.7487 - val_loss: 0.8936 - val_accuracy: 0.6984 Epoch 24/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.7072 - accuracy: 0.7504 - val_loss: 0.8872 - val_accuracy: 0.7002 Running privacy report for epoch: 24 Epoch 25/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6932 - accuracy: 0.7570 - val_loss: 0.9732 - val_accuracy: 0.6769 Epoch 26/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6883 - accuracy: 0.7578 - val_loss: 0.9332 - val_accuracy: 0.6798 Running privacy report for epoch: 26 Epoch 27/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6766 - accuracy: 0.7614 - val_loss: 0.9069 - val_accuracy: 0.6998 Epoch 28/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6656 - accuracy: 0.7662 - val_loss: 0.8879 - val_accuracy: 0.7011 Running privacy report for epoch: 28 Epoch 29/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6594 - accuracy: 0.7674 - val_loss: 0.8988 - val_accuracy: 0.7037 Epoch 30/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6499 - accuracy: 0.7700 - val_loss: 0.9086 - val_accuracy: 0.7001 Running privacy report for epoch: 30 Epoch 31/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6420 - accuracy: 0.7746 - val_loss: 0.8985 - val_accuracy: 0.7034 Epoch 32/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6354 - accuracy: 0.7742 - val_loss: 0.9089 - val_accuracy: 0.7018 Running privacy report for epoch: 32 Epoch 33/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6293 - accuracy: 0.7759 - val_loss: 0.9258 - val_accuracy: 0.6947 Epoch 34/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6192 - accuracy: 0.7851 - val_loss: 0.9326 - val_accuracy: 0.6976 Running privacy report for epoch: 34 Epoch 35/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6157 - accuracy: 0.7831 - val_loss: 0.9240 - val_accuracy: 0.6973 Epoch 36/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6063 - accuracy: 0.7853 - val_loss: 0.9504 - val_accuracy: 0.6971 Running privacy report for epoch: 36 Epoch 37/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.6036 - accuracy: 0.7867 - val_loss: 0.9025 - val_accuracy: 0.7094 Epoch 38/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5958 - accuracy: 0.7877 - val_loss: 0.9290 - val_accuracy: 0.6976 Running privacy report for epoch: 38 Epoch 39/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5900 - accuracy: 0.7919 - val_loss: 0.9379 - val_accuracy: 0.6963 Epoch 40/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5856 - accuracy: 0.7928 - val_loss: 0.9911 - val_accuracy: 0.6896 Running privacy report for epoch: 40 Epoch 41/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5772 - accuracy: 0.7944 - val_loss: 0.9093 - val_accuracy: 0.7059 Epoch 42/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5752 - accuracy: 0.7940 - val_loss: 0.9275 - val_accuracy: 0.7061 Running privacy report for epoch: 42 Epoch 43/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5645 - accuracy: 0.7998 - val_loss: 0.9208 - val_accuracy: 0.7025 Epoch 44/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5632 - accuracy: 0.8000 - val_loss: 0.9746 - val_accuracy: 0.6976 Running privacy report for epoch: 44 Epoch 45/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5557 - accuracy: 0.8045 - val_loss: 0.9211 - val_accuracy: 0.7098 Epoch 46/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5469 - accuracy: 0.8073 - val_loss: 0.9357 - val_accuracy: 0.7055 Running privacy report for epoch: 46 Epoch 47/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5438 - accuracy: 0.8062 - val_loss: 0.9495 - val_accuracy: 0.7025 Epoch 48/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5437 - accuracy: 0.8069 - val_loss: 0.9509 - val_accuracy: 0.6994 Running privacy report for epoch: 48 Epoch 49/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5414 - accuracy: 0.8066 - val_loss: 0.9780 - val_accuracy: 0.6939 Epoch 50/50 1000/1000 [==============================] - 3s 3ms/step - loss: 0.5321 - accuracy: 0.8108 - val_loss: 1.0109 - val_accuracy: 0.6846 Running privacy report for epoch: 50
توطئه های دوران
میتوانید با بررسی دورهای مدل (مثلاً هر 5 دوره)، هنگام آموزش مدلها، خطرات حریم خصوصی را تجسم کنید که میتوانید نقطه زمانی را با بهترین عملکرد/معادل حریم خصوصی انتخاب کنید.
استفاده از TF حریم ماژول عضویت استنتاج حمله به تولید AttackResults
. این AttackResults
از به یک ترکیب AttackResultsCollection
. گزارش TF حریم طراحی شده است به تجزیه و تحلیل ارائه AttackResultsCollection
.
results = AttackResultsCollection(all_reports)
privacy_metrics = (PrivacyMetric.AUC, PrivacyMetric.ATTACKER_ADVANTAGE)
epoch_plot = privacy_report.plot_by_epochs(
results, privacy_metrics=privacy_metrics)
ببینید که به عنوان یک قاعده، با افزایش تعداد دورهها، آسیبپذیری حریم خصوصی بیشتر میشود. این در انواع مدل ها و همچنین انواع مختلف مهاجم صادق است.
مدلهای دو لایه (با لایههای کانولوشنال کمتر) به طور کلی آسیبپذیرتر از مدلهای سه لایهی مشابه خود هستند.
حال بیایید ببینیم که عملکرد مدل با توجه به ریسک حریم خصوصی چگونه تغییر می کند.
حریم خصوصی در مقابل ابزار مفید
privacy_metrics = (PrivacyMetric.AUC, PrivacyMetric.ATTACKER_ADVANTAGE)
utility_privacy_plot = privacy_report.plot_privacy_vs_accuracy(
results, privacy_metrics=privacy_metrics)
for axis in utility_privacy_plot.axes:
axis.set_xlabel('Validation accuracy')
مدلهای سه لایه (شاید به دلیل پارامترهای زیاد) تنها به دقت قطار 0.85 دست مییابند. مدلهای دو لایه عملکرد تقریباً برابری را برای آن سطح از خطر حفظ حریم خصوصی به دست میآورند، اما همچنان به دقت بهتری دست مییابند.
شما همچنین می توانید ببینید که چگونه خط برای مدل های دو لایه شیب دار تر می شود. این بدان معنی است که دستاوردهای حاشیه ای اضافی در دقت قطار به قیمت آسیب پذیری های گسترده حریم خصوصی است.
این پایان آموزش است. با خیال راحت نتایج خود را تجزیه و تحلیل کنید.