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Kaggle首席技术官发布——(Kaggle)NIPS 2017对抗学习挑战赛起步指南
NIPS12 NIPSNIPS 2017Competition Track23Ian GoodfellowAdversarial Attacks and Defences()
kaggle
Inception V3googleNet Inception V3
Alextensorflow
/
import os
from cleverhans.attacks import FastGradientMethod
from io import BytesIO
import IPython.display
import numpy as np
import pandas as pd
from PIL import Image
from scipy.misc import imread
from scipy.misc import imsave
import tensorflow as tf
from tensorflow.contrib.slim.nets import inception
slim = tf.contrib.slim
tensorflow_master = ""
checkpoint_path = "../input/inception-v3/inception_v3.ckpt"
input_dir = "../input/nips-2017-adversarial-learning-development-set/images/"
max_epsilon = 16.0
image_width = 299
image_height = 299
batch_size = 16
eps = 2.0 * max_epsilon / 255.0
batch_shape = [batch_size, image_height, image_width, 3]
num_classes = 1001
def load_images(input_dir, batch_shape):
images = np.zeros(batch_shape)
filenames = []
idx = 0
batch_size = batch_shape[0]
for filepath in sorted(tf.gfile.Glob(os.path.join(input_dir, '*.png'))):
with tf.gfile.Open(filepath, "rb") as f:
images[idx, :, :, :] = imread(f, mode='RGB').astype(np.float)*2.0/255.0 - 1.0
filenames.append(os.path.basename(filepath))
idx += 1
if idx == batch_size:
yield filenames, images
filenames = []
images = np.zeros(batch_shape)
idx = 0
if idx > 0:
yield filenames, images
def show_image(a, fmt='png'):
a = np.uint8((a+1.0)/2.0*255.0)
f = BytesIO()
Image.fromarray(a).save(f, fmt)
IPython.display.display(IPython.display.Image(data=f.getvalue()))
class InceptionModel(object):
def __init__(self, num_classes):
self.num_classes = num_classes
self.built = False
def __call__(self, x_input):
"""Constructs model and return probabilities for given input."""
reuse = True if self.built else None
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(
x_input, num_classes=self.num_classes, is_training=False,
reuse=reuse)
self.built = True
output = end_points['Predictions']
probs = output.op.inputs[0]
return probs
categories = pd.read_csv("../input/nips-2017-adversarial-learning-development-set/categories.csv")
image_classes = pd.read_csv("../input/nips-2017-adversarial-learning-development-set/images.csv")
image_iterator = load_images(input_dir, batch_shape)
# get first batch of images
filenames, images = next(image_iterator)
image_metadata = pd.DataFrame({"ImageId": [f[:-4] for f in filenames]}).merge(image_classes,
on="ImageId")
true_classes = image_metadata["TrueLabel"].tolist()
target_classes = true_labels = image_metadata["TargetClass"].tolist()
true_classes_names = (pd.DataFrame({"CategoryId": true_classes})
.merge(categories, on="CategoryId")["CategoryName"].tolist())
target_classes_names = (pd.DataFrame({"CategoryId": target_classes})
.merge(categories, on="CategoryId")["CategoryName"].tolist())
print("Here's an example of one of the images in the development set")
show_image(images[0])
tensorflow
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
model = InceptionModel(num_classes)
fgsm = FastGradientMethod(model)
x_adv = fgsm.generate(x_input, eps=eps, clip_min=-1., clip_max=1.)
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=checkpoint_path,
master=tensorflow_master)
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
nontargeted_images = sess.run(x_adv, feed_dict={x_input: images})
print("The original image is on the left, and the nontargeted adversarial image is on the right. They look very similar, don't they? It's very clear both are gondolas")
show_image(np.concatenate([images[1], nontargeted_images[1]], axis=1))
INFO:tensorflow:Restoring parameters from ../input/inception-v3/inception_v3.ckpt
tensorflow
all_images_target_class = {image_metadata["ImageId"][i]+".png": image_metadata["TargetClass"][i]
for i in image_metadata.index}
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_input, num_classes=num_classes, is_training=False)
target_class_input = tf.placeholder(tf.int32, shape=[batch_size])
one_hot_target_class = tf.one_hot(target_class_input, num_classes)
cross_entropy = tf.losses.softmax_cross_entropy(one_hot_target_class,
logits,
label_smoothing=0.1,
weights=1.0)
cross_entropy += tf.losses.softmax_cross_entropy(one_hot_target_class,
end_points['AuxLogits'],
label_smoothing=0.1,
weights=0.4)
x_adv = x_input - eps * tf.sign(tf.gradients(cross_entropy, x_input)[0])
x_adv = tf.clip_by_value(x_adv, -1.0, 1.0)
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=checkpoint_path,
master=tensorflow_master)
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
target_class_for_batch = ([all_images_target_class[n] for n in filenames]
+ [0] * (batch_size - len(filenames)))
targeted_images = sess.run(x_adv,
feed_dict={x_input: images,
target_class_input: target_class_for_batch})
print("The original image is on the left, and the targeted adversarial image is on the right. Again, they look very similar, don't they? It's very clear both are butterflies")
show_image(np.concatenate([images[2], targeted_images[2]], axis=1))
INFO:tensorflow:Restoring parameters from ../input/inception-v3/inception_v3.ckpt
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(x_input, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=checkpoint_path,
master=tensorflow_master)
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
predicted_classes = sess.run(predicted_labels, feed_dict={x_input: images})
predicted_nontargeted_classes = sess.run(predicted_labels, feed_dict={x_input: nontargeted_images})
predicted_targeted_classes = sess.run(predicted_labels, feed_dict={x_input: targeted_images})
predicted_classes_names = (pd.DataFrame({"CategoryId": predicted_classes})
.merge(categories, on="CategoryId")["CategoryName"].tolist())
predicted_nontargeted_classes_names = (pd.DataFrame({"CategoryId": predicted_nontargeted_classes})
.merge(categories, on="CategoryId")["CategoryName"].tolist())
predicted_targeted_classes_names = (pd.DataFrame({"CategoryId": predicted_targeted_classes})
.merge(categories, on="CategoryId")["CategoryName"].tolist())
INFO:tensorflow:Restoring parameters from ../input/inception-v3/inception_v3.ckpt
for i in range(len(images)):
print("UNMODIFIED IMAGE (left)",
"\n\tPredicted class:", predicted_classes_names[i],
"\n\tTrue class: ", true_classes_names[i])
print("NONTARGETED ADVERSARIAL IMAGE (center)",
"\n\tPredicted class:", predicted_nontargeted_classes_names[i])
print("TARGETED ADVERSARIAL IMAGE (right)",
"\n\tPredicted class:", predicted_targeted_classes_names[i],
"\n\tTarget class: ", target_classes_names[i])
show_image(np.concatenate([images[i], nontargeted_images[i], targeted_images[i]], axis=1))
Ben HamnerKaggle
Linkedinhttps://www.linkedin.com/in/ben-hamner-98759712/
Github: https://github.com/benhamner
Getting Started with the NIPS 2017 Adversarial Learning ChallengesBen Hamner
最后更新：2017-07-14 09:04:04