1. Break through the reCAPTCHA with Tensorflow API
in AI-Project
Break through the security program for prevent ing macros, reCAPTCHA , using pretrained model[Inception_Net]. As you can see in the picture below, Inception_Net makes it easy to find a bus.
Break through the security program for prevent ing macros, reCAPTCHA , using Inception Net.
main code
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import re
import sys
import tarfile
import numpy as np
from six.moves import urllib
import tensorflow as tf
# pylint: disable=line-too-long
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
# pylint: enable=line-too-long
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self,
label_lookup_path=None,
uid_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(
'model', 'imagenet_2012_challenge_label_map_proto.pbtxt')
if not uid_lookup_path:
uid_lookup_path = os.path.join(
'model', 'imagenet_synset_to_human_label_map.txt')
self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
def load(self, label_lookup_path, uid_lookup_path):
"""Loads a human readable English name for each softmax node.
Args:
label_lookup_path: string UID to integer node ID.
uid_lookup_path: string UID to human-readable string.
Returns:
dict from integer node ID to human-readable string.
"""
if not tf.gfile.Exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not tf.gfile.Exists(label_lookup_path):
tf.logging.fatal('File does not exist %s', label_lookup_path)
# Loads mapping from string UID to human-readable string
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
p = re.compile(r'[n\d]*[ \S,]*')
for line in proto_as_ascii_lines:
parsed_items = p.findall(line)
uid = parsed_items[0]
human_string = parsed_items[2]
uid_to_human[uid] = human_string
# Loads mapping from string UID to integer node ID.
node_id_to_uid = {}
proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
for line in proto_as_ascii:
if line.startswith(' target_class:'):
target_class = int(line.split(': ')[1])
if line.startswith(' target_class_string:'):
target_class_string = line.split(': ')[1]
node_id_to_uid[target_class] = target_class_string[1:-2]
# Loads the final mapping of integer node ID to human-readable string
node_id_to_name = {}
for key, val in node_id_to_uid.items():
if val not in uid_to_human:
tf.logging.fatal('Failed to locate: %s', val)
name = uid_to_human[val]
node_id_to_name[key] = name
return node_id_to_name
def id_to_string(self, node_id):
if node_id not in self.node_lookup:
return ''
return self.node_lookup[node_id]
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(os.path.join(
'model', 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
def run_inference_on_image(image):
"""Runs inference on an image.
Args:
image: Image file name.
Returns:
Nothing
"""
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
create_graph()
with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-5:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
def maybe_download_and_extract():
"""Download and extract model tar file."""
dest_directory = 'model'
if not os.path.exists(dest_directory):
os.makedirs(dest_directory)
filename = DATA_URL.split('/')[-1]
filepath = os.path.join(dest_directory, filename)
if not os.path.exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %s %.1f%%' % (
filename, float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
tarfile.open(filepath, 'r:gz').extractall(dest_directory)
import PIL.Image as pilimg
img= pilimg.open('image/reCAPTCHA0.png')
img
load model
default model » DATA_URL = ‘http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz’
any model you want » DATA_URL = ‘XXX’
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
maybe_download_and_extract()
image classification
img= pilimg.open('image/reCAPTCHA1.png')
img
run_inference_on_image('image/reCAPTCHA1.png')
moving van (score = 0.73877)
golfcart, golf cart (score = 0.04019)
mobile home, manufactured home (score = 0.01723)
trolleybus, trolley coach, trackless trolley (score = 0.01485)
recreational vehicle, RV, R.V. (score = 0.01232)
img= pilimg.open('image/reCAPTCHA2.png')
img
run_inference_on_image('image/reCAPTCHA2.png')
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi (score = 0.26690)
seashore, coast, seacoast, sea-coast (score = 0.08148)
screen, CRT screen (score = 0.06434)
moving van (score = 0.04228)
mobile home, manufactured home (score = 0.03318)
img= pilimg.open('image/reCAPTCHA3.png')
img
run_inference_on_image('image/reCAPTCHA3.png')
minivan (score = 0.27316)
limousine, limo (score = 0.15485)
convertible (score = 0.03864)
beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon (score = 0.03772)
moving van (score = 0.03242)
img= pilimg.open('image/reCAPTCHA4.png')
img
run_inference_on_image('image/reCAPTCHA4.png')
airliner (score = 0.09025)
trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi (score = 0.08313)
traffic light, traffic signal, stoplight (score = 0.08120)
minivan (score = 0.07874)
tow truck, tow car, wrecker (score = 0.05683)
img= pilimg.open('image/reCAPTCHA5.png')
img
run_inference_on_image('image/reCAPTCHA5.png')
seashore, coast, seacoast, sea-coast (score = 0.29206)
aircraft carrier, carrier, flattop, attack aircraft carrier (score = 0.08607)
lakeside, lakeshore (score = 0.06951)
airliner (score = 0.05057)
mobile home, manufactured home (score = 0.03011)
img= pilimg.open('image/reCAPTCHA6.png')
img
run_inference_on_image('image/reCAPTCHA6.png')
moving van (score = 0.18085)
seashore, coast, seacoast, sea-coast (score = 0.15978)
mobile home, manufactured home (score = 0.11563)
steel arch bridge (score = 0.04356)
worm fence, snake fence, snake-rail fence, Virginia fence (score = 0.03528)
img= pilimg.open('image/reCAPTCHA7.png')
img
run_inference_on_image('image/reCAPTCHA7.png')
school bus (score = 0.94535)
amphibian, amphibious vehicle (score = 0.00103)
trolleybus, trolley coach, trackless trolley (score = 0.00101)
passenger car, coach, carriage (score = 0.00091)
streetcar, tram, tramcar, trolley, trolley car (score = 0.00088)
img= pilimg.open('image/reCAPTCHA8.png')
img
run_inference_on_image('image/reCAPTCHA8.png')
seashore, coast, seacoast, sea-coast (score = 0.23562)
sandbar, sand bar (score = 0.10286)
yawl (score = 0.07008)
airliner (score = 0.03950)
promontory, headland, head, foreland (score = 0.02806)
img= pilimg.open('image/reCAPTCHA9.png')
img
run_inference_on_image('image/reCAPTCHA9.png')
school bus (score = 0.92765)
moving van (score = 0.00156)
trolleybus, trolley coach, trackless trolley (score = 0.00152)
tobacco shop, tobacconist shop, tobacconist (score = 0.00088)
thresher, thrasher, threshing machine (score = 0.00087)
Summary
Conclusion
The picture quality was not very good, but we all correctly determined whether the bus was included. Program using Inception Net will be able to break through the reCAPTCHA.
