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worker.py
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worker.py
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## worker.py -- evaluation code
##
## Copyright (C) 2017, Dongyu Meng <[email protected]>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import matplotlib
matplotlib.use('Agg')
from scipy.stats import entropy
from numpy.linalg import norm
from matplotlib.ticker import FuncFormatter
from keras.models import Sequential, load_model
from keras.activations import softmax
from keras.layers import Lambda
import numpy as np
import pylab
import os
from utils import prepare_data
import utils
import matplotlib.pyplot as plt
#from setup_cifar import CIFAR, CIFARModel
from setup_mnist import MNIST, MNISTModel
import tensorflow as tf
class AEDetector:
def __init__(self, path, p=1):
"""
Error based detector.
Marks examples for filtering decisions.
path: Path to the autoencoder used.
p: Distance measure to use.
"""
self.model = load_model(path)
self.path = path
self.p = p
def mark(self, X):
diff = np.abs(X - self.model.predict(X))
# print(diff.shape)
marks = np.mean(np.power(diff, self.p), axis=(1,2,3))
# print(marks.shape)
# print('aed',marks)
return marks
def print(self):
return "AEDetector:" + self.path.split("/")[-1]
class IdReformer:
def __init__(self, path="IdentityFunction"):
"""
Identity reformer.
Reforms an example to itself.
"""
self.path = path
self.heal = lambda X: X
def print(self):
return "IdReformer:" + self.path
class SimpleReformer:
def __init__(self, path):
"""
Reformer.
Reforms examples with autoencoder. Action of reforming is called heal.
path: Path to the autoencoder used.
"""
self.model = load_model(path)
self.path = path
def heal(self, X):
X = self.model.predict(X)
return np.clip(X, 0.0, 1.0)
def print(self):
return "SimpleReformer:" + self.path.split("/")[-1]
def JSD(P, Q):
_P = P / norm(P, ord=1)
_Q = Q / norm(Q, ord=1)
_M = 0.5 * (_P + _Q)
return 0.5 * (entropy(_P, _M) + entropy(_Q, _M))
class DBDetector:
def __init__(self, reconstructor, prober, classifier, option="jsd", T=1):
"""
Divergence-Based Detector.
reconstructor: One autoencoder.
prober: Another autoencoder.
classifier: Classifier object.
option: Measure of distance, jsd as default.
T: Temperature to soften the classification decision.
"""
self.prober = prober
self.reconstructor = reconstructor
self.classifier = classifier
self.option = option
self.T = T
def mark(self, X):
return self.mark_jsd(X)
def mark_jsd(self, X):
Xp = self.prober.heal(X)
Xr = self.reconstructor.heal(X)
Pp = self.classifier.classify(Xp, option="prob", T=self.T)
Pr = self.classifier.classify(Xr, option="prob", T=self.T)
marks = [(JSD(Pp[i], Pr[i])) for i in range(len(Pr))]
return np.array(marks)
def print(self):
return "Divergence-Based Detector"
class Classifier:
def __init__(self, classifier_path):
"""
Keras classifier wrapper.
Note that the wrapped classifier should spit logits as output.
classifier_path: Path to Keras classifier file.
"""
self.path = classifier_path
# with tf.Session() as sess:
self.model = MNISTModel(classifier_path)
self.softmax = Sequential()
self.softmax.add(Lambda(lambda X: softmax(X, axis=1), input_shape=(10,)))
def classify(self, X, option="logit", T=1):
if option == "logit":
# print(X)
return self.model.model.predict(X)
if option == "prob":
logits = self.model.predict(X)/T
return self.softmax.predict(logits)
def print(self):
return "Classifier:"+self.path.split("/")[-1]
class Operator:
def __init__(self, data, classifier, det_dict, reformer):
"""
Operator.
Describes the classification problem and defense.
data: Standard problem dataset. Including train, test, and validation.
classifier: Target classifier.
reformer: Reformer of defense.
det_dict: Detector(s) of defense.
"""
self.data = data
self.classifier = classifier
self.det_dict = det_dict
self.reformer = reformer
# self.normal = self.operate(AttackData(self.data.test_data,
# np.argmax(self.data.test_labels, axis=1), "Normal"))
def get_thrs(self, drop_rate):
"""
Get filtering threshold by marking validation set.
"""
thrs = dict()
for name, detector in self.det_dict.items():
num = int(len(self.data.validation_data) * drop_rate[name])
marks = detector.mark(self.data.validation_data)
# print(self.data.validation_data[0])
marks = np.sort(marks)
thrs[name] = marks[-num]
print(thrs)
return thrs
def operate(self, untrusted_obj):
"""
For untrusted input(normal or adversarial), classify original input and
reformed input. Classifier is unaware of the source of input.
untrusted_obj: Input data.
"""
X = untrusted_obj.adv_data
print(X.shape)
Y_true = np.squeeze(untrusted_obj.origin_label)
# print(Y_true)
X_prime = self.reformer.heal(X)
# Y = np.argmax(self.classifier.classify(X), axis=1)
Y = np.argmax(Y_true, axis = 1)
Y = np.repeat(Y,9)
# print(Y)
# Y_judgement = (Y == Y_true[:len(X_prime)])
Y_prime = np.argmax(self.classifier.classify(X_prime), axis=1)
# print(Y_prime)
error = 0
for i in range(len(Y)):
if Y_prime[i] != Y[i]:
error += 1
print(error)
# Y_prime_judgement = (np.array(Y_prime == Y_true)
# print(Y_prime_judgement)
# return np.array(list(zip(Y_judgement, Y_prime_judgement)))
def filter(self, X, thrs):
"""
untrusted_obj: Untrusted input to test against.
thrs: Thresholds.
return:
all_pass: Index of examples that passed all detectors.
collector: Number of examples that escaped each detector.
"""
collector = dict()
all_pass = np.array(range(X.shape[0]))
for name, detector in self.det_dict.items():
marks = detector.mark(X)
print(np.max(marks))
idx_pass = np.argwhere(marks < thrs[name])
# print('idx_pass', len(idx_pass))
# collector[name] = len(idx_pass)
all_pass = np.intersect1d(all_pass, idx_pass)
# print('all pass:',all_pass.shape)
pas = all_pass.shape[0]/X.shape[0]
return pas
def print(self):
components = [self.reformer, self.classifier]
return " ".join(map(lambda obj: getattr(obj, "print")(), components))
class AttackData:
def __init__(self, examples, labels, name=""):
"""
Input data wrapper. May be normal or adversarial.
examples: Path or object of input examples.
labels: Ground truth labels.
"""
if isinstance(examples, str): self.data = utils.load_obj(examples)
else: self.data = examples
self.labels = labels
self.name = name
def print(self):
return "Attack:"+self.name
class Evaluator:
def __init__(self, operator, untrusted_data, graph_dir="./graph"):
"""
Evaluator.
For strategy described by operator, conducts tests on untrusted input.
Mainly stats and plotting code. Most methods omitted for clarity.
operator: Operator object.
untrusted_data: Input data to test against.
graph_dir: Where to spit the graphs.
"""
self.operator = operator
self.untrusted_data = untrusted_data
self.graph_dir = graph_dir
self.data_package = operator.operate(untrusted_data)
def bind_operator(self, operator):
self.operator = operator
self.data_package = operator.operate(self.untrusted_data)
def load_data(self, data):
self.untrusted_data = data
self.data_package = self.operator.operate(self.untrusted_data)
def get_normal_acc(self, normal_all_pass):
"""
Break down of who does what in defense. Accuracy of defense on normal
input.
both: Both detectors and reformer take effect
det_only: detector(s) take effect
ref_only: Only reformer takes effect
none: Attack effect with no defense
"""
normal_tups = self.operator.normal
num_normal = len(normal_tups)
filtered_normal_tups = normal_tups[normal_all_pass]
both_acc = sum(1 for _, XpC in filtered_normal_tups if XpC)/num_normal
det_only_acc = sum(1 for XC, XpC in filtered_normal_tups if XC)/num_normal
ref_only_acc = sum([1 for _, XpC in normal_tups if XpC])/num_normal
none_acc = sum([1 for XC, _ in normal_tups if XC])/num_normal
return both_acc, det_only_acc, ref_only_acc, none_acc
def get_attack_acc(self, attack_pass):
attack_tups = self.data_package
num_untrusted = len(attack_tups)
filtered_attack_tups = attack_tups[attack_pass]
both_acc = 1 - sum(1 for _, XpC in filtered_attack_tups if not XpC)/num_untrusted
det_only_acc = 1 - sum(1 for XC, XpC in filtered_attack_tups if not XC)/num_untrusted
ref_only_acc = sum([1 for _, XpC in attack_tups if XpC])/num_untrusted
none_acc = sum([1 for XC, _ in attack_tups if XC])/num_untrusted
return both_acc, det_only_acc, ref_only_acc, none_acc
def plot_various_confidences(self, graph_name, drop_rate,
idx_file="example_idx",
confs=(0.0, 10.0, 20.0, 30.0, 40.0),
get_attack_data_name=lambda c: "example_carlini_"+str(c)):
"""
Test defense performance against Carlini L2 attack of various confidences.
graph_name: Name of graph file.
drop_rate: How many normal examples should each detector drops?
idx_file: Index of adversarial examples in standard test set.
confs: A series of confidence to test against.
get_attack_data_name: Function mapping confidence to corresponding file.
"""
pylab.rcParams['figure.figsize'] = 6, 4
fig = plt.figure(1, (6, 4))
ax = fig.add_subplot(1, 1, 1)
idx = utils.load_obj(idx_file)
X, _, Y = prepare_data(self.operator.data, idx)
det_only = []
ref_only = []
both = []
none = []
print("\n==========================================================")
print("Drop Rate:", drop_rate)
thrs = self.operator.get_thrs(drop_rate)
all_pass, _ = self.operator.filter(self.operator.data.test_data, thrs)
all_on_acc, _, _, _ = self.get_normal_acc(all_pass)
print("Classification accuracy with all defense on:", all_on_acc)
for confidence in confs:
f = get_attack_data_name(confidence)
self.load_data(AttackData(f, Y, "Carlini L2 " + str(confidence)))
print("----------------------------------------------------------")
print("Confidence:", confidence)
all_pass, detector_breakdown = self.operator.filter(self.untrusted_data.data, thrs)
both_acc, det_only_acc, ref_only_acc, none_acc = self.get_attack_acc(all_pass)
print(detector_breakdown)
both.append(both_acc)
det_only.append(det_only_acc)
ref_only.append(ref_only_acc)
none.append(none_acc)
size = 2.5
plt.plot(confs, none, c="green", label="No fefense", marker="x", markersize=size)
plt.plot(confs, det_only, c="orange", label="With detector", marker="o", markersize=size)
plt.plot(confs, ref_only, c="blue", label="With reformer", marker="^", markersize=size)
plt.plot(confs, both, c="red", label="With detector & reformer", marker="s", markersize=size)
pylab.legend(loc='lower left', bbox_to_anchor=(0.02, 0.1), prop={'size':8})
plt.grid(linestyle='dotted')
plt.xlabel(r"Confidence in Carlini $L^2$ attack")
plt.ylabel("Classification accuracy")
plt.xlim(min(confs)-1.0, max(confs)+1.0)
plt.ylim(-0.05, 1.05)
ax.yaxis.set_major_formatter(FuncFormatter('{0:.0%}'.format))
save_path = os.path.join(self.graph_dir, graph_name+".pdf")
plt.savefig(save_path)
plt.clf()
def print(self):
return " ".join([self.operator.print(), self.untrusted_data.print()])