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outlier_class_training.py
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outlier_class_training.py
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## outlier_class_training.py -- break an outlier class detector
##
## Copyright (C) 2017, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras import regularizers
import utils
import random
import tensorflow as tf
from setup_mnist import MNIST, MNISTModel
from setup_cifar import CIFAR, CIFARModel
import os
from keras import backend as K
from fast_gradient_sign import FGS
from nn_robust_attacks.l2_attack import CarliniL2
def train(Model, data, num_labels, file_name, num_epochs=50, batch_size=128):
"""
Standard neural network training procedure.
"""
model = Model(num_labels=num_labels).model
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn,
optimizer=sgd,
metrics=['accuracy'])
model.fit(data.train_data, data.train_labels,
batch_size=batch_size,
validation_data=(data.validation_data, data.validation_labels),
epochs=num_epochs,
shuffle=True)
acc = np.mean(np.argmax(model.predict(data.test_data),axis=1)==np.argmax(data.test_labels,axis=1))
print("Overall accuracy on test set:", acc)
if file_name != None:
model.save(file_name)
return model
class Wrap:
def __init__(self, model, image_size, num_channels, num_labels):
self.image_size = image_size
self.num_channels = num_channels
self.num_labels = num_labels
self.model = model
def predict(self, xs):
return self.model(xs)
def run_evaluation(Data, Model, path, num_epochs, name):
data = Data()
#train(Model, data, 10, path, num_epochs=num_epochs)
sess = K.get_session()
K.set_learning_phase(False)
model = Model(path)
#attack = FGS(sess, model)
attack = CarliniL2(sess, model, batch_size=100, max_iterations=3000,
binary_search_steps=3, targeted=True, initial_const=10, learning_rate=1e-2)
""" # uncomment to run the training phase
train_adv = attack.attack(data.train_data, data.train_labels)
np.save("tmp/"+name+"outlieradvtrain",train_adv)
train_adv = np.load("tmp/"+name+"outlieradvtrain.npy")
data.train_data = np.concatenate((data.train_data, train_adv))
data.train_labels = np.concatenate((data.train_labels, np.zeros(data.train_labels.shape, dtype=np.float32)))
data.train_labels = np.pad(data.train_labels, [[0, 0], [0, 1]], mode='constant')
data.train_labels[data.train_labels.shape[0]//2:,10] = 1
validation_adv = attack.attack(data.validation_data, data.validation_labels)
np.save("tmp/"+name+"outlieradvvalidation",validation_adv)
validation_adv = np.load("tmp/"+name+"outlieradvvalidation.npy")
data.validation_data = np.concatenate((data.validation_data, validation_adv))
data.validation_labels = np.concatenate((data.validation_labels, np.zeros(data.validation_labels.shape, dtype=np.float32)))
data.validation_labels = np.pad(data.validation_labels, [[0, 0], [0, 1]], mode='constant')
data.validation_labels[data.validation_labels.shape[0]//2:,10] = 1
test_adv = attack.attack(data.test_data, data.test_labels)
np.save("tmp/"+name+"outlieradvtest",test_adv)
test_adv = np.load("tmp/"+name+"outlieradvtest.npy")
data.test_data = np.concatenate((data.test_data, test_adv))
data.test_labels = np.concatenate((data.test_labels, np.zeros(data.test_labels.shape, dtype=np.float32)))
data.test_labels = np.pad(data.test_labels, [[0, 0], [0, 1]], mode='constant')
data.test_labels[data.test_labels.shape[0]//2:,10] = 1
train(Model, data, 11, path+"_advtraining", num_epochs=num_epochs)
data1 = Data() # just need a reference, this is a bit ugly to do
data2 = Data() # just need a reference, this is a bit ugly to do
idxs = list(range(len(data.train_data)))
random.shuffle(idxs)
data1.train_data = data.train_data[idxs[:len(idxs)//2]]
data2.train_data = data.train_data[idxs[len(idxs)//2:]]
data1.train_labels = data.train_labels[idxs[:len(idxs)//2],:]
data2.train_labels = data.train_labels[idxs[len(idxs)//2:],:]
idxs = list(range(len(data.validation_data)))
random.shuffle(idxs)
data1.validation_data = data.validation_data[idxs[:len(idxs)//2]]
data2.validation_data = data.validation_data[idxs[len(idxs)//2:]]
data1.validation_labels = data.validation_labels[idxs[:len(idxs)//2]]
data2.validation_labels = data.validation_labels[idxs[len(idxs)//2:]]
idxs = list(range(len(data.test_data)))
random.shuffle(idxs)
data1.test_data = data.test_data[idxs[:len(idxs)//2]]
data2.test_data = data.test_data[idxs[len(idxs)//2:]]
data1.test_labels = data.test_labels[idxs[:len(idxs)//2]]
data2.test_labels = data.test_labels[idxs[len(idxs)//2:]]
train(Model, data1, 11, path+"_advtraining-left", num_epochs=num_epochs)
train(Model, data2, 11, path+"_advtraining-right", num_epochs=num_epochs)
#"""
K.set_learning_phase(False)
rmodel = Model(num_labels=11).model
rmodel.load_weights(path+"_advtraining")
if name == "cifar":
rmodel = Wrap(rmodel, 32, 3, 11)
else:
rmodel = Wrap(rmodel, 28, 1, 11)
rmodel1 = Model(num_labels=11).model
rmodel1.load_weights(path+"_advtraining-left")
if name == "cifar":
rmodel1 = Wrap(rmodel1, 32, 3, 11)
else:
rmodel1 = Wrap(rmodel1, 28, 1, 11)
rmodel2 = Model(num_labels=11).model
rmodel2.load_weights(path+"_advtraining-right")
if name == "cifar":
rmodel2 = Wrap(rmodel2, 32, 3, 11)
else:
rmodel2 = Wrap(rmodel2, 28, 1, 11)
rmodel2.model.summary()
attack2 = CarliniL2(sess, rmodel, batch_size=100, max_iterations=2000, confidence=.1,
binary_search_steps=3, targeted=True, initial_const=10, learning_rate=1e-2)
#test_adv = np.load("tmp/outlieradvtest.npy")
#print('qq',np.mean(rmodel.model.predict_classes(test_adv)==10))
N = 100
targets = utils.get_labs(data.test_labels[:100])
#"""
test_adv = attack.attack(data.test_data[:N], targets)
print('mean distortion',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
print('model predict',np.argmax(model.model.predict(test_adv),axis=1))
print('rmodel predict',np.argmax(rmodel.model.predict(test_adv),axis=1))
#"""
targets2 = np.zeros((N, 11))
targets2[:, :10] = targets
test_adv = attack2.attack(data.test_data[:N], targets2)
print(list(test_adv[0].flatten()))
print('mean distortion',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
a=(np.argmax(model.model.predict(test_adv),axis=1))
#print(a)
print('summary',np.mean(a==np.argmax(targets,axis=1)),np.mean(a==10))
a=(np.argmax(rmodel.model.predict(test_adv),axis=1))
#print(a)
print('summary',np.mean(a==np.argmax(targets,axis=1)),np.mean(a==10))
a=(np.argmax(rmodel1.model.predict(test_adv),axis=1))
#print(a)
print('summary',np.mean(a==np.argmax(targets,axis=1)),np.mean(a==10))
a=(np.argmax(rmodel2.model.predict(test_adv),axis=1))
#print(a)
print('summary',np.mean(a==np.argmax(targets,axis=1)),np.mean(a==10))
run_evaluation(MNIST, MNISTModel, "models/mnist", num_epochs=30, name="mnist")
run_evaluation(CIFAR, CIFARModel, "models/cifar", num_epochs=100, name="cifar")