forked from 626_privacy/tensorflow_privacy
176 lines
6.5 KiB
Python
176 lines
6.5 KiB
Python
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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"""Run auditing on the FashionMNIST dataset."""
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import numpy as np
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import tensorflow.compat.v1 as tf
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from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
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from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
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from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
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from absl import app
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from absl import flags
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import audit
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#### FLAGS
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FLAGS = flags.FLAGS
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flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
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flags.DEFINE_float('noise_multiplier', 1.1,
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'Ratio of the standard deviation to the clipping norm')
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flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
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flags.DEFINE_integer('batch_size', 250, 'Batch size')
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flags.DEFINE_integer('epochs', 24, 'Number of epochs')
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flags.DEFINE_integer(
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'microbatches', 250, 'Number of microbatches '
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'(must evenly divide batch_size)')
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flags.DEFINE_string('model', 'lr', 'model to use, pick between lr and nn')
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flags.DEFINE_string('attack_type', "clip_aware", 'clip_aware or bkdr')
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flags.DEFINE_integer('pois_ct', 1, 'Number of poisoning points')
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flags.DEFINE_integer('num_trials', 100, 'Number of trials for auditing')
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flags.DEFINE_float('attack_l2_norm', 10, 'Size of poisoning data')
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flags.DEFINE_float('alpha', 0.05, '1-confidence')
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flags.DEFINE_boolean('load_weights', False,
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'if True, use weights saved in init_weights.h5')
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FLAGS = flags.FLAGS
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def compute_epsilon(train_size):
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"""Computes epsilon value for given hyperparameters."""
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if FLAGS.noise_multiplier == 0.0:
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return float('inf')
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orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
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sampling_probability = FLAGS.batch_size / train_size
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steps = FLAGS.epochs * train_size / FLAGS.batch_size
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rdp = compute_rdp(q=sampling_probability,
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noise_multiplier=FLAGS.noise_multiplier,
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steps=steps,
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orders=orders)
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# Delta is set to approximate 1 / (number of training points).
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return get_privacy_spent(orders, rdp, target_delta=1e-5)[0]
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def build_model(x, y):
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"""Build a keras model."""
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input_shape = x.shape[1:]
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num_classes = y.shape[1]
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l2 = 0
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if FLAGS.model == 'lr':
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model = tf.keras.Sequential([
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tf.keras.layers.Flatten(input_shape=input_shape),
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tf.keras.layers.Dense(num_classes, kernel_initializer='glorot_normal',
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kernel_regularizer=tf.keras.regularizers.l2(l2))
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])
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elif FLAGS.model == 'nn':
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model = tf.keras.Sequential([
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tf.keras.layers.Flatten(input_shape=input_shape),
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tf.keras.layers.Dense(32, activation='relu',
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kernel_initializer='glorot_normal',
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kernel_regularizer=tf.keras.regularizers.l2(l2)),
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tf.keras.layers.Dense(num_classes, kernel_initializer='glorot_normal',
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kernel_regularizer=tf.keras.regularizers.l2(l2))
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])
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else:
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raise NotImplementedError
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return model
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def train_model(model, train_x, train_y, save_weights=False):
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"""Train the model on given data."""
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optimizer = dp_optimizer_vectorized.VectorizedDPSGD(
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l2_norm_clip=FLAGS.l2_norm_clip,
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noise_multiplier=FLAGS.noise_multiplier,
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num_microbatches=FLAGS.microbatches,
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learning_rate=FLAGS.learning_rate)
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loss = tf.keras.losses.CategoricalCrossentropy(
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from_logits=True, reduction=tf.losses.Reduction.NONE)
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# Compile model with Keras
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model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
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if save_weights:
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wts = model.get_weights()
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np.save('save_model', wts)
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model.set_weights(wts)
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return model
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if FLAGS.load_weights: # load preset weights
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wts = np.load('save_model.npy', allow_pickle=True).tolist()
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model.set_weights(wts)
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# Train model with Keras
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model.fit(train_x, train_y,
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epochs=FLAGS.epochs,
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validation_data=(train_x, train_y),
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batch_size=FLAGS.batch_size,
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verbose=0)
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return model
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def membership_test(model, pois_x, pois_y):
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"""Membership inference - detect poisoning."""
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probs = model.predict(np.concatenate([pois_x, np.zeros_like(pois_x)]))
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return np.multiply(probs[0, :] - probs[1, :], pois_y).sum()
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def train_and_score(dataset):
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"""Complete training run with membership inference score."""
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x, y, pois_x, pois_y, i = dataset
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np.random.seed(i)
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tf.set_random_seed(i)
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tf.reset_default_graph()
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model = build_model(x, y)
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model = train_model(model, x, y)
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return membership_test(model, pois_x, pois_y)
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def main(unused_argv):
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del unused_argv
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# Load training and test data.
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np.random.seed(0)
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(train_x, train_y), _ = tf.keras.datasets.fashion_mnist.load_data()
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train_inds = np.where(train_y < 2)[0]
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train_x = -.5 + train_x[train_inds] / 255.
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train_y = np.eye(2)[train_y[train_inds]]
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# subsample dataset
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ss_inds = np.random.choice(train_x.shape[0], train_x.shape[0]//2, replace=False)
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train_x = train_x[ss_inds]
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train_y = train_y[ss_inds]
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init_model = build_model(train_x, train_y)
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_ = train_model(init_model, train_x, train_y, save_weights=True)
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auditor = audit.AuditAttack(train_x, train_y, train_and_score)
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thresh, _, _ = auditor.run(FLAGS.pois_ct, FLAGS.attack_type, FLAGS.num_trials,
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alpha=FLAGS.alpha, threshold=None,
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l2_norm=FLAGS.attack_l2_norm)
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_, eps, acc = auditor.run(FLAGS.pois_ct, FLAGS.attack_type, FLAGS.num_trials,
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alpha=FLAGS.alpha, threshold=thresh,
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l2_norm=FLAGS.attack_l2_norm)
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epsilon_upper_bound = compute_epsilon(train_x.shape[0])
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print("Analysis epsilon is {}.".format(epsilon_upper_bound))
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print("At threshold={}, epsilon={}.".format(thresh, eps))
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print("The best accuracy at distinguishing poisoning is {}.".format(acc))
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if __name__ == '__main__':
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app.run(main)
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