Merge branch 'dev'

Conflicts:
	config/__init__.py
	examples/cifar_adversarial_training.py
	examples/cifar_feature_squeezing.py
	examples/mnist_cnn_fgsm.py
	examples/mnist_transferability.py
	generate_adversarial.py
	generate_from_imagenet.py
	notebooks/visual-layers.ipynb
	run_tests.sh
	src/attacks/attack.py
	src/attacks/carlini.py
	src/attacks/carlini_unittest.py
	src/attacks/deepfool.py
	src/attacks/universal_perturbation.py
	src/attacks/virtual_adversarial.py
	src/classifiers/classifier.py
	src/classifiers/cnn_unittest.py
	src/classifiers/mlp.py
	src/classifiers/mlp_unittest.py
	src/classifiers/resnet.py
	src/classifiers/resnet_unittest.py
	src/classifiers/siamese.py
	src/classifiers/siamese_unittest.py
	src/classifiers/utils.py
	src/layers/activations.py
	src/layers/activations_unittest.py
	src/metrics.py
	src/metrics_unittest.py
	src/utils.py
	train_adversarially.py
	train_with_noise.py

Author: Irina Nicolae

.gitignore

@@ -90,3 +90,7 @@ ENV/
 
 # ignore local config
 *config.ini
+
+# ignore pictures
+*.jpg
+demo/pics/*

config/__init__.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import, division, print_function
+
 from os.path import abspath, dirname, expanduser, join
 import sys
 

examples/cifar_adversarial_training.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
 """Trains a convolutional neural network on the CIFAR-10 dataset, then generated adversarial images using the
-Carlini & Wagner method and retrains the network on the training set augmented with the adversarial images.
+DeepFool attack and retrains the network on the training set augmented with the adversarial images.
 
 Gets to 56.80% accuracy on the adversarial samples after data augmentation over 10 epochs.
 """
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
+
 from os.path import abspath
 import sys
 sys.path.append(abspath('.'))

examples/cifar_feature_squeezing.py

@@ -3,7 +3,8 @@
 
 Gets to 70.04% test accuracy after 10 epochs.
 """
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
+
 from os.path import abspath
 import sys
 sys.path.append(abspath('.'))

examples/mnist_cnn_fgsm.py

@@ -3,7 +3,8 @@
 
 With 5 epochs of training, gets to 98.89% accuracy on the test data and 65.40% on the adversarial examples.
 """
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
+
 from os.path import abspath
 import sys
 sys.path.append(abspath('.'))

examples/mnist_transferability.py

@@ -5,7 +5,8 @@
 
 The CNN obtains 98.57% accuracy on the adversarial samples when models are fitted for 5 epochs.
 """
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
+
 from os.path import abspath
 import sys
 sys.path.append(abspath('.'))

generate_adversarial.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import, division, print_function
+
 from config import config_dict
 
 import numpy as np

requirements.txt

@@ -1,26 +1,4 @@
-appdirs==1.4.3
-click==6.7
-cycler==0.10.0
-Flask==0.12.2
-h5py==2.7.0
-itsdangerous==0.24
-Jinja2==2.9.6
 Keras==2.0.4
-MarkupSafe==1.0
 matplotlib==2.0.1
-numpy==1.12.1
-olefile==0.44
-packaging==16.8
-picasso-viz==0.1.1
-Pillow==4.1.1
-protobuf==3.3.0
-pyparsing==2.2.0
-python-dateutil==2.6.0
-pytz==2017.2
-PyYAML==3.12
-requests==2.14.2
 scipy==0.19.0
-six==1.10.0
 tensorflow==1.1.0
-Theano==0.9.0
-Werkzeug==0.12.1

src/attacks/attack.py

@@ -1,4 +1,7 @@
-from abc import ABCMeta
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+import sys
 
 import numpy as np
 import tensorflow as tf
@@ -32,12 +35,17 @@ def class_derivative(preds, x, num_labels=10):
     """
     return [tf.gradients(preds[:, i], x) for i in range(num_labels)]
 
+# Ensure compatibility with Python 2 and 3 when using ABCMeta
+if sys.version_info >= (3, 4):
+    ABC = abc.ABC
+else:
+    ABC = abc.ABCMeta('ABC', (), {})
+
 
-class Attack:
+class Attack(ABC):
     """
     Abstract base class for all attack classes. Adapted from cleverhans (https://github.com/openai/cleverhans).
     """
-    __metaclass__ = ABCMeta
     attack_params = ['classifier', 'session']
 
     def __init__(self, classifier, sess=None):

src/attacks/carlini.py

@@ -1,4 +1,7 @@
+from __future__ import absolute_import, division, print_function
+
 from config import config_dict
+
 from keras.utils.np_utils import to_categorical
 import tensorflow as tf