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Repository: dongyp13/Translation-Invariant-Attacks
Branch: master
Commit: c3ae1ba061ff
Files: 40
Total size: 641.2 KB
Directory structure:
gitextract_blf6ykcn/
├── LICENSE
├── README.md
├── attack_iter.py
├── attack_vgg16_mim.py
├── dataset/
│ ├── README.md
│ ├── dev_dataset.csv
│ └── download_images.py
├── metadata.json
├── nets/
│ ├── __init__.py
│ ├── alexnet.py
│ ├── alexnet_test.py
│ ├── cifarnet.py
│ ├── inception.py
│ ├── inception_resnet_v2.py
│ ├── inception_resnet_v2_test.py
│ ├── inception_utils.py
│ ├── inception_v1.py
│ ├── inception_v1_test.py
│ ├── inception_v2.py
│ ├── inception_v2_test.py
│ ├── inception_v3.py
│ ├── inception_v3_test.py
│ ├── inception_v4.py
│ ├── inception_v4_test.py
│ ├── lenet.py
│ ├── mobilenet_v1.md
│ ├── mobilenet_v1.py
│ ├── mobilenet_v1_test.py
│ ├── nets_factory.py
│ ├── nets_factory_test.py
│ ├── overfeat.py
│ ├── overfeat_test.py
│ ├── resnet_utils.py
│ ├── resnet_v1.py
│ ├── resnet_v1_test.py
│ ├── resnet_v2.py
│ ├── resnet_v2_test.py
│ ├── vgg.py
│ └── vgg_test.py
└── run_attack.sh
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
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================================================
FILE: README.md
================================================
# Translation-Invariant Attacks
## Introduction
This repository contains the code for [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://arxiv.org/pdf/1904.02884.pdf) (CVPR 2019 Oral).
## Method
We proposed a translation-invariant (TI) attack method to generate more transferable adversarial examples. This method is implemented by convolving the gradient with a pre-defined kernel in each attack iteration, and can be integrated into any gradient-based attack method.
## Run the code
First download the [models](#Models). You can also use other models by changing the model definition part in the code.
Then run the following command
```
bash run_attack.sh input_dir output_dir 16
```
where original images are stored in ``input_dir`` with ``.png`` format, and the generated adversarial images are saved in ``output_dir``.
We used the Python 2.7 and Tensorflow 1.12 versions.
## Results
We consider eight SOTA defense models on ImageNet:
* Inc-v3<sub>ens3</sub>, Inc-v3<sub>ens4</sub>, IncRes-v2<sub>ens</sub> trained by [Ensemble Adversarial Training](https://arxiv.org/abs/1705.07204);
* [High-level representation guided denoiser](https://arxiv.org/abs/1712.02976) (HGD, top-1 submission in the NIPS 2017 defense competition);
* [Input transformation through random resizing and padding](https://arxiv.org/abs/1711.01991) (R&P, rank-2 submission in the NIPS 2017 defense competition);
* [Input transformation through JPEG compression or total variance minimization (TVM)](https://openreview.net/pdf?id=SyJ7ClWCb);
* [Rank-3 submission in the NIPS 2017 defense competition (NIPS-r3)](https://github.com/anlthms/nips-2017/tree/master/mmd);
We attacked these models by the [fast gradient sign method](https://arxiv.org/abs/1412.6572) (FGSM), [momentum iterative fast gradient sign method](https://arxiv.org/abs/1710.06081) (MI-FGSM), [diverse input method](https://arxiv.org/abs/1803.06978) (DIM), and their translation-invariant versions as TI-FGSM, TI-MI-FGSM, and TI-DIM. We generated adversarial examples for the ensemble of Inception V3, Inception V4, Inception ResNet V2, and ResNet V2 152 with epsilon 16. The success rates against the eight defenses are:
<img src="https://github.com/dongyp13/Translation-Invariant-Attacks/blob/master/results.png">
### Citation
If you use our method for attacks in your research, please consider citing
@inproceedings{dong2019evading,
title={Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks},
author={Dong, Yinpeng and Pang, Tianyu and Su, Hang and Zhu, Jun},
booktitle={Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
year={2019}
}
## Implementation
### Models
The models can be downloaded at [Inception V3](http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/inception_v3.ckpt), [Inception V4](http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/inception_v4.ckpt), [Inception ResNet V2](http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/inception_resnet_v2_2016_08_30.ckpt), and [ResNet V2 152](http://ml.cs.tsinghua.edu.cn/~yinpeng/downloads/resnet_v2_152.ckpt).
If you want to attack other models, you can replace the model definition part to your own models.
### Hyper-parameters
* For TI-FGSM, set ``num_iter=1``, ``momentum=0.0``, ``prob=0.0``;
* For TI-MI-FGSM, set ``num_iter=10``, ``momentum=1.0``, ``prob=0.0``;
* For TI-DIM, set ``num_iter=10``, ``momentum=1.0``, ``prob=0.7``;
================================================
FILE: attack_iter.py
================================================
"""Implementation of sample attack."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
start_time = time.time()
import numpy as np
from scipy.misc import imread
from scipy.misc import imsave
from scipy.misc import imresize
import tensorflow as tf
from nets import inception_v3, inception_v4, inception_resnet_v2, resnet_v2
slim = tf.contrib.slim
tf.flags.DEFINE_string('master', '', 'The address of the TensorFlow master to use.')
tf.flags.DEFINE_string('checkpoint_path_inception_v3', '', 'Path to checkpoint for inception network.')
tf.flags.DEFINE_string('checkpoint_path_inception_v4', '', 'Path to checkpoint for inception network.')
tf.flags.DEFINE_string('checkpoint_path_inception_resnet_v2', '', 'Path to checkpoint for inception network.')
tf.flags.DEFINE_string('checkpoint_path_resnet', '', 'Path to checkpoint for inception network.')
tf.flags.DEFINE_string('input_dir', '', 'Input directory with images.')
tf.flags.DEFINE_string('output_dir', '', 'Output directory with images.')
tf.flags.DEFINE_float('max_epsilon', 32.0, 'Maximum size of adversarial perturbation.')
tf.flags.DEFINE_integer('num_iter', 10, 'Number of iterations.')
tf.flags.DEFINE_integer('image_width', 299, 'Width of each input images.')
tf.flags.DEFINE_integer('image_height', 299, 'Height of each input images.')
tf.flags.DEFINE_integer('image_resize', 330, 'Height of each input images.')
tf.flags.DEFINE_integer('batch_size', 10, 'How many images process at one time.')
tf.flags.DEFINE_float('momentum', 1.0, 'Momentum.')
tf.flags.DEFINE_float('prob', 0.4, 'probability of using diverse inputs.')
FLAGS = tf.flags.FLAGS
def gkern(kernlen=21, nsig=3):
"""Returns a 2D Gaussian kernel array."""
import scipy.stats as st
x = np.linspace(-nsig, nsig, kernlen)
kern1d = st.norm.pdf(x)
kernel_raw = np.outer(kern1d, kern1d)
kernel = kernel_raw / kernel_raw.sum()
return kernel
kernel = gkern(15, 3).astype(np.float32)
stack_kernel = np.stack([kernel, kernel, kernel]).swapaxes(2, 0)
stack_kernel = np.expand_dims(stack_kernel, 3)
def load_images(input_dir, batch_shape):
"""Read png images from input directory in batches.
Args:
input_dir: input directory
batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3]
Yields:
filenames: list file names without path of each image
Lenght of this list could be less than batch_size, in this case only
first few images of the result are elements of the minibatch.
images: array with all images from this batch
"""
images = np.zeros(batch_shape)
filenames = []
idx = 0
batch_size = batch_shape[0]
for filepath in tf.gfile.Glob(os.path.join(input_dir, '*.png')):
with tf.gfile.Open(filepath) as f:
image = imresize(imread(f, mode='RGB'), [FLAGS.image_height, FLAGS.image_width]).astype(np.float) / 255.0
# Images for inception classifier are normalized to be in [-1, 1] interval.
images[idx, :, :, :] = image * 2.0 - 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 save_images(images, filenames, output_dir):
"""Saves images to the output directory.
Args:
images: array with minibatch of images
filenames: list of filenames without path
If number of file names in this list less than number of images in
the minibatch then only first len(filenames) images will be saved.
output_dir: directory where to save images
"""
for i, filename in enumerate(filenames):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# so rescale them back to [0, 1].
with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:
imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')
def graph(x, y, i, x_max, x_min, grad):
eps = 2.0 * FLAGS.max_epsilon / 255.0
num_iter = FLAGS.num_iter
alpha = eps / num_iter
momentum = FLAGS.momentum
num_classes = 1001
# should keep original x here for output
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits_v3, end_points_v3 = inception_v3.inception_v3(
input_diversity(x), num_classes=num_classes, is_training=False)
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits_v4, end_points_v4 = inception_v4.inception_v4(
input_diversity(x), num_classes=num_classes, is_training=False)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits_res_v2, end_points_res_v2 = inception_resnet_v2.inception_resnet_v2(
input_diversity(x), num_classes=num_classes, is_training=False, reuse=True)
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet, end_points_resnet = resnet_v2.resnet_v2_152(
input_diversity(x), num_classes=num_classes, is_training=False)
logits = (logits_v3 + logits_v4 + logits_res_v2 + logits_resnet) / 4
auxlogits = (end_points_v3['AuxLogits'] + end_points_v4['AuxLogits'] + end_points_res_v2['AuxLogits']) / 3
cross_entropy = tf.losses.softmax_cross_entropy(y,
logits,
label_smoothing=0.0,
weights=1.0)
cross_entropy += tf.losses.softmax_cross_entropy(y,
auxlogits,
label_smoothing=0.0,
weights=0.4)
noise = tf.gradients(cross_entropy, x)[0]
noise = tf.nn.depthwise_conv2d(noise, stack_kernel, strides=[1, 1, 1, 1], padding='SAME')
noise = noise / tf.reduce_mean(tf.abs(noise), [1, 2, 3], keep_dims=True)
noise = momentum * grad + noise
x = x + alpha * tf.sign(noise)
x = tf.clip_by_value(x, x_min, x_max)
i = tf.add(i, 1)
return x, y, i, x_max, x_min, noise
def stop(x, y, i, x_max, x_min, grad):
num_iter = FLAGS.num_iter
return tf.less(i, num_iter)
def input_diversity(input_tensor):
rnd = tf.random_uniform((), FLAGS.image_width, FLAGS.image_resize, dtype=tf.int32)
rescaled = tf.image.resize_images(input_tensor, [rnd, rnd], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
h_rem = FLAGS.image_resize - rnd
w_rem = FLAGS.image_resize - rnd
pad_top = tf.random_uniform((), 0, h_rem, dtype=tf.int32)
pad_bottom = h_rem - pad_top
pad_left = tf.random_uniform((), 0, w_rem, dtype=tf.int32)
pad_right = w_rem - pad_left
padded = tf.pad(rescaled, [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]], constant_values=0.)
padded.set_shape((input_tensor.shape[0], FLAGS.image_resize, FLAGS.image_resize, 3))
return tf.cond(tf.random_uniform(shape=[1])[0] < tf.constant(FLAGS.prob), lambda: padded, lambda: input_tensor)
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
num_classes = 1001
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
tf.logging.set_verbosity(tf.logging.INFO)
print(time.time() - start_time)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_max = tf.clip_by_value(x_input + eps, -1.0, 1.0)
x_min = tf.clip_by_value(x_input - eps, -1.0, 1.0)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
_, end_points = inception_resnet_v2.inception_resnet_v2(
x_input, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
y = tf.one_hot(predicted_labels, num_classes)
i = tf.constant(0)
grad = tf.zeros(shape=batch_shape)
x_adv, _, _, _, _, _ = tf.while_loop(stop, graph, [x_input, y, i, x_max, x_min, grad])
# Run computation
s1 = tf.train.Saver(slim.get_model_variables(scope='InceptionV3'))
s5 = tf.train.Saver(slim.get_model_variables(scope='InceptionV4'))
s6 = tf.train.Saver(slim.get_model_variables(scope='InceptionResnetV2'))
s8 = tf.train.Saver(slim.get_model_variables(scope='resnet_v2'))
with tf.Session() as sess:
s1.restore(sess, FLAGS.checkpoint_path_inception_v3)
s5.restore(sess, FLAGS.checkpoint_path_inception_v4)
s6.restore(sess, FLAGS.checkpoint_path_inception_resnet_v2)
s8.restore(sess, FLAGS.checkpoint_path_resnet)
print(time.time() - start_time)
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
save_images(adv_images, filenames, FLAGS.output_dir)
print(time.time() - start_time)
if __name__ == '__main__':
tf.app.run()
================================================
FILE: attack_vgg16_mim.py
================================================
"""Implementation of sample attack."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import numpy as np
from scipy.misc import imread
from scipy.misc import imsave, imresize
import tensorflow as tf
from nets import vgg
slim = tf.contrib.slim
_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94
tf.flags.DEFINE_string(
'master', '', 'The address of the TensorFlow master to use.')
tf.flags.DEFINE_string(
'checkpoint_path_vgg', '', 'Path to checkpoint for inception network.')
tf.flags.DEFINE_string(
'input_dir', '', 'Input directory with images.')
tf.flags.DEFINE_string(
'output_dir', '', 'Output directory with images.')
tf.flags.DEFINE_float(
'max_epsilon', 16.0, 'Maximum size of adversarial perturbation.')
tf.flags.DEFINE_integer(
'num_iter', 10, 'Number of iterations.')
tf.flags.DEFINE_integer(
'image_width', 224, 'Width of each input images.')
tf.flags.DEFINE_integer(
'image_height', 224, 'Height of each input images.')
tf.flags.DEFINE_integer(
'batch_size', 16, 'How many images process at one time.')
tf.flags.DEFINE_float(
'momentum', 0.0, 'Momentum.')
FLAGS = tf.flags.FLAGS
def gkern(kernlen=21, nsig=3):
"""Returns a 2D Gaussian kernel array."""
import scipy.stats as st
x = np.linspace(-nsig, nsig, kernlen)
kern1d = st.norm.pdf(x)
kernel_raw = np.outer(kern1d, kern1d)
kernel = kernel_raw / kernel_raw.sum()
return kernel
kernel = gkern(15, 3).astype(np.float32)
stack_kernel = np.stack([kernel, kernel, kernel]).swapaxes(2, 0)
stack_kernel = np.expand_dims(stack_kernel, 3)
def load_images(input_dir, batch_shape):
"""Read png images from input directory in batches.
Args:
input_dir: input directory
batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3]
Yields:
filenames: list file names without path of each image
Lenght of this list could be less than batch_size, in this case only
first few images of the result are elements of the minibatch.
images: array with all images from this batch
"""
images = np.zeros(batch_shape)
filenames = []
idx = 0
batch_size = batch_shape[0]
for filepath in tf.gfile.Glob(os.path.join(input_dir, '*.png')):
with tf.gfile.Open(filepath) as f:
image = imread(f, mode='RGB')
image = imresize(image, [FLAGS.image_height, FLAGS.image_width]).astype(np.float)
image[:,:,0] -= _R_MEAN
image[:,:,1] -= _G_MEAN
image[:,:,2] -= _B_MEAN
images[idx, :, :, :] = image
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 save_images(images, filenames, output_dir):
"""Saves images to the output directory.
Args:
images: array with minibatch of images
filenames: list of filenames without path
If number of file names in this list less than number of images in
the minibatch then only first len(filenames) images will be saved.
output_dir: directory where to save images
"""
for i, filename in enumerate(filenames):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# so rescale them back to [0, 1].
with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:
image = images[i, :, :, :]
image[:,:,0] += _R_MEAN
image[:,:,1] += _G_MEAN
image[:,:,2] += _B_MEAN
image = imresize(image, [299, 299])
imsave(f, image, format='png')
def graph(x, y, i, x_max, x_min, grad):
eps = FLAGS.max_epsilon
num_iter = FLAGS.num_iter
alpha = eps / num_iter
momentum = FLAGS.momentum
num_classes = 1000
with slim.arg_scope(vgg.vgg_arg_scope()):
logits, end_points = vgg.vgg_16(
x, num_classes=num_classes, is_training=False)
pred = tf.argmax(logits, 1)
first_round = tf.cast(tf.equal(i, 0), tf.int64)
y = first_round * pred + (1 - first_round) * y
one_hot = tf.one_hot(y, num_classes)
cross_entropy = tf.losses.softmax_cross_entropy(one_hot,
logits,
label_smoothing=0.0,
weights=1.0)
noise = tf.gradients(cross_entropy, x)[0]
noise = tf.nn.depthwise_conv2d(noise, stack_kernel, strides=[1, 1, 1, 1], padding='SAME')
noise = noise / tf.reduce_mean(tf.abs(noise), [1,2,3], keep_dims=True)
noise = momentum * grad + noise
x = x + alpha * tf.sign(noise)
x = tf.clip_by_value(x, x_min, x_max)
i = tf.add(i, 1)
return x, y, i, x_max, x_min, noise
def stop(x, y, i, x_max, x_min, grad):
num_iter = FLAGS.num_iter
return tf.less(i, num_iter)
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
start_time = time.time()
eps = FLAGS.max_epsilon
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_max = x_input + eps
x_min = x_input - eps
y = tf.constant(np.zeros([FLAGS.batch_size]), tf.int64)
i = tf.constant(0)
grad = tf.zeros(shape=batch_shape)
x_adv, _, _, _, _, _ = tf.while_loop(stop, graph, [x_input, y, i, x_max, x_min, grad])
# Run computation
s1 = tf.train.Saver(slim.get_model_variables())
print('Building Graph Done', time.time() - start_time)
with tf.Session() as sess:
s1.restore(sess, FLAGS.checkpoint_path_vgg)
print('Load Parameters Done', time.time() - start_time)
tot_images = 0
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
save_images(adv_images, filenames, FLAGS.output_dir)
tot_images += len(filenames)
print(tot_images, time.time() - start_time)
print(time.time() - start_time)
if __name__ == '__main__':
tf.app.run()
================================================
FILE: dataset/README.md
================================================
# Dataset for adversarial competition.
Two datasets will be used for the competition:
* **DEV** dataset which is available here for development and experimenting.
* **TEST** dataset which will be kept secret until after the competition
and will be used for final scoring.
Both datasets are composed from publicly available images which were posted
online under CC-BY license.
## Dataset format
DEV dataset is defined by `dev_dataset.csv`
which contains URLs of the images along with bounding boxes
and classification labels.
`dev_dataset.csv` is a table in
[CSV](https://en.wikipedia.org/wiki/Comma-separated_values)
format with the following columns:
* **ImageId** - id of the image.
* **URL** - URL of the image.
* **x1**, **y1**, **x2**, **y2** - bounding box of the area of interest in
the image. Bounding box is relative, which means that all coordinates are
between 0 and 1.
* **TrueLabel** - true label of the image.
* **TargetClass** - label for targeted adversarial attack.
* **OriginalLandingURL** - original landing page where this image was found.
* **License** - licence under which image was distributed by author.
* **Author** - author of the image.
* **AuthorProfileURL** - URL of the author's profile.
Dataset is labelled with
[ImageNet](http://www.image-net.org/challenges/LSVRC/2012/) labels.
Specific values of labels are compatible with pre-trained Inception models,
which are available as a part of
[TF-Slim](https://github.com/tensorflow/models/tree/master/slim).
In particular pre-trained Inception v3 and InceptionResnet v2 could be used
to classify dataset with high accuracy.
## Downloading images
`dev_dataset.csv` contains only URLs of the images.
Actual images have to be downloaded before being used for experiments.
`download_images.py` is a Python program which downloads images for all
records in `dev_dataset.csv`. Usage:
```
# Replace CSV_FILE with path to dev_dataset.csv
CSV_FILE=dev_dataset.csv
# Replace OUTPUT_DIR with path to directory where all images should be stored
OUTPUT_DIR=images
# Download images
python download_images.py --input_file=${CSV_FILE} --output_dir=${OUTPUT_DIR}
```
All downloaded images will be cropped according to the bounding boxes in
`dev_dataset.csv` and resized to 299x299 pixels.
Each image will be saved in PNG format with filename `IMAGE_ID.png`
where `IMAGE_ID` is the id of the image from `dev_dataset.csv`.
================================================
FILE: dataset/dev_dataset.csv
================================================
ImageId,URL,x1,y1,x2,y2,TrueLabel,TargetClass,OriginalLandingURL,License,Author,AuthorProfileURL
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696f0f6bea562bf8,https://c1.staticflickr.com/6/5605/30947139580_468ba7e513_o.jpg,0,0,0.5,0.75,854,267,https://www.flickr.com/photos/jamesbradley/30947139580/,https://creativecommons.org/licenses/by/2.0/,Fr James Bradley,https://www.flickr.com/people/jamesbradley/
df58f94361c6d105,https://c1.staticflickr.com/8/7248/27047266920_8363816754_o.jpg,0.200627477,0,0.601882431,0.75,610,13,https://www.flickr.com/photos/roberthaandrikman/27047266920,https://creativecommons.org/licenses/by/2.0/,Robert Haandrikman,https://www.flickr.com/people/roberthaandrikman/
1394faa319bd353c,https://c1.staticflickr.com/1/542/31667350163_b6906e0d48_o.jpg,0.333333333,0,1,1,610,507,https://www.flickr.com/photos/landrovermena/31667350163,https://creativecommons.org/licenses/by/2.0/,Land Rover MENA,https://www.flickr.com/people/landrovermena/
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137ab6ca314e9e35,https://c1.staticflickr.com/9/8681/28102039640_11703a03f5_o.jpg,0,0.311985173,0.75,0.935955519,620,556,https://www.flickr.com/photos/130468093@N03/28102039640,https://creativecommons.org/licenses/by/2.0/,~ladyyoruichi~,https://www.flickr.com/people/130468093@N03/
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4b384c8247b56c0a,https://c1.staticflickr.com/9/8526/28626152335_955398ef80_o.jpg,0,0.25,0.5,1,542,925,https://www.flickr.com/photos/virtualpainter/28626152335,https://creativecommons.org/licenses/by/2.0/,Taka Umemura,https://www.flickr.com/people/virtualpainter/
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a2de1f4f2c1021fa,https://c1.staticflickr.com/9/8598/29005552090_ee38f841ac_o.jpg,0.25,0.25,1,1,456,185,https://www.flickr.com/photos/sillygwailo/29005552090/,https://creativecommons.org/licenses/by/2.0/,Richard Eriksson,https://www.flickr.com/people/sillygwailo/
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1a264165276e3c85,https://c1.staticflickr.com/8/7327/27500591886_a1b257229e_o.jpg,0.5,0,1,0.75,855,759,https://www.flickr.com/photos/presidentialoffice/27500591886,https://creativecommons.org/licenses/by/2.0/,\347\270\275\347\265\261\345\272\234,https://www.flickr.com/people/presidentialoffice/
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01bdc0cfe670f708,https://c1.staticflickr.com/1/457/31837933011_3e57f56dc9_o.jpg,0,0,0.5,0.75,472,541,https://www.flickr.com/photos/quinet/31837933011,https://creativecommons.org/licenses/by/2.0/,Thomas Quine,https://www.flickr.com/people/quinet/
6af829a442e2e0e7,https://c1.staticflickr.com/6/5827/30663702381_910aca914a_o.jpg,0.25,0,1,1,130,231,https://www.flickr.com/photos/rusty_clark/30663702381,https://creativecommons.org/licenses/by/2.0/,Rusty Clark ~ 100K Photos,https://www.flickr.com/people/rusty_clark/
493694833dc23399,https://c1.staticflickr.com/1/691/33087466756_27fcc6e909_o.jpg,0.25,0.25,0.75,1,538,835,https://www.flickr.com/photos/denalinps/33087466756/,https://creativecommons.org/licenses/by/2.0/,Denali National Park and Preserve,https://www.flickr.com/people/denalinps/
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gitextract_blf6ykcn/ ├── LICENSE ├── README.md ├── attack_iter.py ├── attack_vgg16_mim.py ├── dataset/ │ ├── README.md │ ├── dev_dataset.csv │ └── download_images.py ├── metadata.json ├── nets/ │ ├── __init__.py │ ├── alexnet.py │ ├── alexnet_test.py │ ├── cifarnet.py │ ├── inception.py │ ├── inception_resnet_v2.py │ ├── inception_resnet_v2_test.py │ ├── inception_utils.py │ ├── inception_v1.py │ ├── inception_v1_test.py │ ├── inception_v2.py │ ├── inception_v2_test.py │ ├── inception_v3.py │ ├── inception_v3_test.py │ ├── inception_v4.py │ ├── inception_v4_test.py │ ├── lenet.py │ ├── mobilenet_v1.md │ ├── mobilenet_v1.py │ ├── mobilenet_v1_test.py │ ├── nets_factory.py │ ├── nets_factory_test.py │ ├── overfeat.py │ ├── overfeat_test.py │ ├── resnet_utils.py │ ├── resnet_v1.py │ ├── resnet_v1_test.py │ ├── resnet_v2.py │ ├── resnet_v2_test.py │ ├── vgg.py │ └── vgg_test.py └── run_attack.sh
SYMBOL INDEX (250 symbols across 31 files)
FILE: attack_iter.py
function gkern (line 55) | def gkern(kernlen=21, nsig=3):
function load_images (line 69) | def load_images(input_dir, batch_shape):
function save_images (line 102) | def save_images(images, filenames, output_dir):
function graph (line 119) | def graph(x, y, i, x_max, x_min, grad):
function stop (line 164) | def stop(x, y, i, x_max, x_min, grad):
function input_diversity (line 169) | def input_diversity(input_tensor):
function main (line 184) | def main(_):
FILE: attack_vgg16_mim.py
function gkern (line 56) | def gkern(kernlen=21, nsig=3):
function load_images (line 72) | def load_images(input_dir, batch_shape):
function save_images (line 108) | def save_images(images, filenames, output_dir):
function graph (line 130) | def graph(x, y, i, x_max, x_min, grad):
function stop (line 160) | def stop(x, y, i, x_max, x_min, grad):
function main (line 165) | def main(_):
FILE: dataset/download_images.py
function parse_args (line 39) | def parse_args():
function download_image (line 51) | def download_image(image_id, url, x1, y1, x2, y2, output_dir):
function main (line 75) | def main():
FILE: nets/alexnet.py
function alexnet_v2_arg_scope (line 45) | def alexnet_v2_arg_scope(weight_decay=0.0005):
function alexnet_v2 (line 55) | def alexnet_v2(inputs,
FILE: nets/alexnet_test.py
class AlexnetV2Test (line 27) | class AlexnetV2Test(tf.test.TestCase):
method testBuild (line 29) | def testBuild(self):
method testFullyConvolutional (line 40) | def testFullyConvolutional(self):
method testEndPoints (line 51) | def testEndPoints(self):
method testModelVariables (line 72) | def testModelVariables(self):
method testEvaluation (line 99) | def testEvaluation(self):
method testTrainEvalWithReuse (line 111) | def testTrainEvalWithReuse(self):
method testForward (line 134) | def testForward(self):
FILE: nets/cifarnet.py
function cifarnet (line 28) | def cifarnet(images, num_classes=10, is_training=False,
function cifarnet_arg_scope (line 93) | def cifarnet_arg_scope(weight_decay=0.004):
FILE: nets/inception_resnet_v2.py
function block35 (line 33) | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=...
function block17 (line 54) | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=...
function block8 (line 74) | def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=N...
function inception_resnet_v2_base (line 94) | def inception_resnet_v2_base(inputs,
function inception_resnet_v2 (line 270) | def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
function inception_resnet_v2_arg_scope (line 331) | def inception_resnet_v2_arg_scope(weight_decay=0.00004,
FILE: nets/inception_resnet_v2_test.py
class InceptionTest (line 25) | class InceptionTest(tf.test.TestCase):
method testBuildLogits (line 27) | def testBuildLogits(self):
method testBuildWithoutAuxLogits (line 44) | def testBuildWithoutAuxLogits(self):
method testBuildEndPoints (line 57) | def testBuildEndPoints(self):
method testBuildBaseNetwork (line 76) | def testBuildBaseNetwork(self):
method testBuildOnlyUptoFinalEndpoint (line 91) | def testBuildOnlyUptoFinalEndpoint(self):
method testBuildAndCheckAllEndPointsUptoPreAuxLogits (line 108) | def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self):
method testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps (line 134) | def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMap...
method testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight (line 160) | def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight...
method testVariablesSetDevice (line 186) | def testVariablesSetDevice(self):
method testHalfSizeImages (line 202) | def testHalfSizeImages(self):
method testUnknownBatchSize (line 216) | def testUnknownBatchSize(self):
method testEvaluation (line 231) | def testEvaluation(self):
method testTrainEvalWithReuse (line 245) | def testTrainEvalWithReuse(self):
FILE: nets/inception_utils.py
function inception_arg_scope (line 32) | def inception_arg_scope(weight_decay=0.00004,
FILE: nets/inception_v1.py
function inception_v1_base (line 29) | def inception_v1_base(inputs,
function inception_v1 (line 257) | def inception_v1(inputs,
FILE: nets/inception_v1_test.py
class InceptionV1Test (line 29) | class InceptionV1Test(tf.test.TestCase):
method testBuildClassificationNetwork (line 31) | def testBuildClassificationNetwork(self):
method testBuildBaseNetwork (line 45) | def testBuildBaseNetwork(self):
method testBuildOnlyUptoFinalEndpoint (line 61) | def testBuildOnlyUptoFinalEndpoint(self):
method testBuildAndCheckAllEndPointsUptoMixed5c (line 78) | def testBuildAndCheckAllEndPointsUptoMixed5c(self):
method testModelHasExpectedNumberOfParameters (line 109) | def testModelHasExpectedNumberOfParameters(self):
method testHalfSizeImages (line 119) | def testHalfSizeImages(self):
method testUnknownImageShape (line 129) | def testUnknownImageShape(self):
method testUnknowBatchSize (line 147) | def testUnknowBatchSize(self):
method testEvaluation (line 164) | def testEvaluation(self):
method testTrainEvalWithReuse (line 179) | def testTrainEvalWithReuse(self):
method testLogitsNotSqueezed (line 196) | def testLogitsNotSqueezed(self):
FILE: nets/inception_v2.py
function inception_v2_base (line 29) | def inception_v2_base(inputs,
function inception_v2 (line 452) | def inception_v2(inputs,
function _reduced_kernel_size_for_small_input (line 525) | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
FILE: nets/inception_v2_test.py
class InceptionV2Test (line 29) | class InceptionV2Test(tf.test.TestCase):
method testBuildClassificationNetwork (line 31) | def testBuildClassificationNetwork(self):
method testBuildBaseNetwork (line 45) | def testBuildBaseNetwork(self):
method testBuildOnlyUptoFinalEndpoint (line 61) | def testBuildOnlyUptoFinalEndpoint(self):
method testBuildAndCheckAllEndPointsUptoMixed5c (line 77) | def testBuildAndCheckAllEndPointsUptoMixed5c(self):
method testModelHasExpectedNumberOfParameters (line 106) | def testModelHasExpectedNumberOfParameters(self):
method testBuildEndPointsWithDepthMultiplierLessThanOne (line 116) | def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
method testBuildEndPointsWithDepthMultiplierGreaterThanOne (line 136) | def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
method testRaiseValueErrorWithInvalidDepthMultiplier (line 156) | def testRaiseValueErrorWithInvalidDepthMultiplier(self):
method testBuildEndPointsWithUseSeparableConvolutionFalse (line 167) | def testBuildEndPointsWithUseSeparableConvolutionFalse(self):
method testBuildEndPointsNCHWDataFormat (line 190) | def testBuildEndPointsNCHWDataFormat(self):
method testBuildErrorsForDataFormats (line 215) | def testBuildErrorsForDataFormats(self):
method testHalfSizeImages (line 229) | def testHalfSizeImages(self):
method testUnknownImageShape (line 243) | def testUnknownImageShape(self):
method testUnknowBatchSize (line 261) | def testUnknowBatchSize(self):
method testEvaluation (line 278) | def testEvaluation(self):
method testTrainEvalWithReuse (line 293) | def testTrainEvalWithReuse(self):
method testLogitsNotSqueezed (line 310) | def testLogitsNotSqueezed(self):
FILE: nets/inception_v3.py
function inception_v3_base (line 29) | def inception_v3_base(inputs,
function inception_v3 (line 419) | def inception_v3(inputs,
function _reduced_kernel_size_for_small_input (line 532) | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
FILE: nets/inception_v3_test.py
class InceptionV3Test (line 29) | class InceptionV3Test(tf.test.TestCase):
method testBuildClassificationNetwork (line 31) | def testBuildClassificationNetwork(self):
method testBuildBaseNetwork (line 45) | def testBuildBaseNetwork(self):
method testBuildOnlyUptoFinalEndpoint (line 62) | def testBuildOnlyUptoFinalEndpoint(self):
method testBuildAndCheckAllEndPointsUptoMixed7c (line 80) | def testBuildAndCheckAllEndPointsUptoMixed7c(self):
method testModelHasExpectedNumberOfParameters (line 112) | def testModelHasExpectedNumberOfParameters(self):
method testBuildEndPoints (line 122) | def testBuildEndPoints(self):
method testBuildEndPointsWithDepthMultiplierLessThanOne (line 146) | def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
method testBuildEndPointsWithDepthMultiplierGreaterThanOne (line 166) | def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
method testRaiseValueErrorWithInvalidDepthMultiplier (line 186) | def testRaiseValueErrorWithInvalidDepthMultiplier(self):
method testHalfSizeImages (line 197) | def testHalfSizeImages(self):
method testUnknownImageShape (line 211) | def testUnknownImageShape(self):
method testUnknowBatchSize (line 228) | def testUnknowBatchSize(self):
method testEvaluation (line 245) | def testEvaluation(self):
method testTrainEvalWithReuse (line 260) | def testTrainEvalWithReuse(self):
method testLogitsNotSqueezed (line 278) | def testLogitsNotSqueezed(self):
FILE: nets/inception_v4.py
function block_inception_a (line 34) | def block_inception_a(inputs, scope=None, reuse=None):
function block_reduction_a (line 55) | def block_reduction_a(inputs, scope=None, reuse=None):
function block_inception_b (line 75) | def block_inception_b(inputs, scope=None, reuse=None):
function block_reduction_b (line 99) | def block_reduction_b(inputs, scope=None, reuse=None):
function block_inception_c (line 121) | def block_inception_c(inputs, scope=None, reuse=None):
function inception_v4_base (line 147) | def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
function inception_v4 (line 257) | def inception_v4(inputs, num_classes=1001, is_training=True,
FILE: nets/inception_v4_test.py
class InceptionTest (line 25) | class InceptionTest(tf.test.TestCase):
method testBuildLogits (line 27) | def testBuildLogits(self):
method testBuildWithoutAuxLogits (line 46) | def testBuildWithoutAuxLogits(self):
method testAllEndPointsShapes (line 58) | def testAllEndPointsShapes(self):
method testBuildBaseNetwork (line 103) | def testBuildBaseNetwork(self):
method testBuildOnlyUpToFinalEndpoint (line 121) | def testBuildOnlyUpToFinalEndpoint(self):
method testVariablesSetDevice (line 139) | def testVariablesSetDevice(self):
method testHalfSizeImages (line 154) | def testHalfSizeImages(self):
method testUnknownBatchSize (line 167) | def testUnknownBatchSize(self):
method testEvaluation (line 182) | def testEvaluation(self):
method testTrainEvalWithReuse (line 196) | def testTrainEvalWithReuse(self):
FILE: nets/lenet.py
function lenet (line 26) | def lenet(images, num_classes=10, is_training=False,
function lenet_arg_scope (line 79) | def lenet_arg_scope(weight_decay=0.0):
FILE: nets/mobilenet_v1.py
function mobilenet_v1_base (line 142) | def mobilenet_v1_base(inputs,
function mobilenet_v1 (line 269) | def mobilenet_v1(inputs,
function wrapped_partial (line 343) | def wrapped_partial(func, *args, **kwargs):
function _reduced_kernel_size_for_small_input (line 354) | def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
function mobilenet_v1_arg_scope (line 376) | def mobilenet_v1_arg_scope(is_training=True,
FILE: nets/mobilenet_v1_test.py
class MobilenetV1Test (line 29) | class MobilenetV1Test(tf.test.TestCase):
method testBuildClassificationNetwork (line 31) | def testBuildClassificationNetwork(self):
method testBuildBaseNetwork (line 45) | def testBuildBaseNetwork(self):
method testBuildOnlyUptoFinalEndpoint (line 70) | def testBuildOnlyUptoFinalEndpoint(self):
method testBuildCustomNetworkUsingConvDefs (line 96) | def testBuildCustomNetworkUsingConvDefs(self):
method testBuildAndCheckAllEndPointsUptoConv2d_13 (line 118) | def testBuildAndCheckAllEndPointsUptoConv2d_13(self):
method testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13 (line 160) | def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self):
method testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13 (line 204) | def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self):
method testBuildAndCheckAllEndPointsApproximateFaceNet (line 248) | def testBuildAndCheckAllEndPointsApproximateFaceNet(self):
method testModelHasExpectedNumberOfParameters (line 291) | def testModelHasExpectedNumberOfParameters(self):
method testBuildEndPointsWithDepthMultiplierLessThanOne (line 302) | def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
method testBuildEndPointsWithDepthMultiplierGreaterThanOne (line 321) | def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
method testRaiseValueErrorWithInvalidDepthMultiplier (line 341) | def testRaiseValueErrorWithInvalidDepthMultiplier(self):
method testHalfSizeImages (line 354) | def testHalfSizeImages(self):
method testUnknownImageShape (line 368) | def testUnknownImageShape(self):
method testUnknowBatchSize (line 386) | def testUnknowBatchSize(self):
method testEvaluation (line 403) | def testEvaluation(self):
method testTrainEvalWithReuse (line 418) | def testTrainEvalWithReuse(self):
method testLogitsNotSqueezed (line 436) | def testLogitsNotSqueezed(self):
FILE: nets/nets_factory.py
function get_network_fn (line 90) | def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
FILE: nets/nets_factory_test.py
class NetworksTest (line 29) | class NetworksTest(tf.test.TestCase):
method testGetNetworkFn (line 31) | def testGetNetworkFn(self):
method testGetNetworkFnArgScope (line 46) | def testGetNetworkFnArgScope(self):
FILE: nets/overfeat.py
function overfeat_arg_scope (line 40) | def overfeat_arg_scope(weight_decay=0.0005):
function overfeat (line 50) | def overfeat(inputs,
FILE: nets/overfeat_test.py
class OverFeatTest (line 27) | class OverFeatTest(tf.test.TestCase):
method testBuild (line 29) | def testBuild(self):
method testFullyConvolutional (line 40) | def testFullyConvolutional(self):
method testEndPoints (line 51) | def testEndPoints(self):
method testModelVariables (line 72) | def testModelVariables(self):
method testEvaluation (line 99) | def testEvaluation(self):
method testTrainEvalWithReuse (line 111) | def testTrainEvalWithReuse(self):
method testForward (line 134) | def testForward(self):
FILE: nets/resnet_utils.py
class Block (line 46) | class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
function subsample (line 59) | def subsample(inputs, factor, scope=None):
function conv2d_same (line 77) | def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=...
function stack_blocks_dense (line 126) | def stack_blocks_dense(net, blocks, output_stride=None,
function resnet_arg_scope (line 199) | def resnet_arg_scope(weight_decay=0.0001,
FILE: nets/resnet_v1.py
function bottleneck (line 69) | def bottleneck(inputs,
function resnet_v1 (line 132) | def resnet_v1(inputs,
function resnet_v1_block (line 234) | def resnet_v1_block(scope, base_depth, num_units, stride):
function resnet_v1_50 (line 258) | def resnet_v1_50(inputs,
function resnet_v1_101 (line 280) | def resnet_v1_101(inputs,
function resnet_v1_152 (line 302) | def resnet_v1_152(inputs,
function resnet_v1_200 (line 324) | def resnet_v1_200(inputs,
FILE: nets/resnet_v1_test.py
function create_test_input (line 30) | def create_test_input(batch_size, height, width, channels):
class ResnetUtilsTest (line 56) | class ResnetUtilsTest(tf.test.TestCase):
method testSubsampleThreeByThree (line 58) | def testSubsampleThreeByThree(self):
method testSubsampleFourByFour (line 65) | def testSubsampleFourByFour(self):
method testConv2DSameEven (line 72) | def testConv2DSameEven(self):
method testConv2DSameOdd (line 113) | def testConv2DSameOdd(self):
method _resnet_plain (line 154) | def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
method testEndPointsV1 (line 162) | def testEndPointsV1(self):
method _stack_blocks_nondense (line 190) | def _stack_blocks_nondense(self, net, blocks):
method testAtrousValuesBottleneck (line 199) | def testAtrousValuesBottleneck(self):
class ResnetCompleteNetworkTest (line 244) | class ResnetCompleteNetworkTest(tf.test.TestCase):
method _resnet_small (line 247) | def _resnet_small(self,
method testClassificationEndPoints (line 274) | def testClassificationEndPoints(self):
method testClassificationShapes (line 289) | def testClassificationShapes(self):
method testFullyConvolutionalEndpointShapes (line 306) | def testFullyConvolutionalEndpointShapes(self):
method testRootlessFullyConvolutionalEndpointShapes (line 324) | def testRootlessFullyConvolutionalEndpointShapes(self):
method testAtrousFullyConvolutionalEndpointShapes (line 343) | def testAtrousFullyConvolutionalEndpointShapes(self):
method testAtrousFullyConvolutionalValues (line 364) | def testAtrousFullyConvolutionalValues(self):
method testUnknownBatchSize (line 391) | def testUnknownBatchSize(self):
method testFullyConvolutionalUnknownHeightWidth (line 411) | def testFullyConvolutionalUnknownHeightWidth(self):
method testAtrousFullyConvolutionalUnknownHeightWidth (line 426) | def testAtrousFullyConvolutionalUnknownHeightWidth(self):
FILE: nets/resnet_v2.py
function bottleneck (line 62) | def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
function resnet_v2 (line 111) | def resnet_v2(inputs,
function resnet_v2_block (line 224) | def resnet_v2_block(scope, base_depth, num_units, stride):
function resnet_v2_50 (line 249) | def resnet_v2_50(inputs,
function resnet_v2_101 (line 271) | def resnet_v2_101(inputs,
function resnet_v2_152 (line 293) | def resnet_v2_152(inputs,
function resnet_v2_200 (line 315) | def resnet_v2_200(inputs,
FILE: nets/resnet_v2_test.py
function create_test_input (line 30) | def create_test_input(batch_size, height, width, channels):
class ResnetUtilsTest (line 56) | class ResnetUtilsTest(tf.test.TestCase):
method testSubsampleThreeByThree (line 58) | def testSubsampleThreeByThree(self):
method testSubsampleFourByFour (line 65) | def testSubsampleFourByFour(self):
method testConv2DSameEven (line 72) | def testConv2DSameEven(self):
method testConv2DSameOdd (line 113) | def testConv2DSameOdd(self):
method _resnet_plain (line 154) | def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
method testEndPointsV2 (line 162) | def testEndPointsV2(self):
method _stack_blocks_nondense (line 190) | def _stack_blocks_nondense(self, net, blocks):
method testAtrousValuesBottleneck (line 199) | def testAtrousValuesBottleneck(self):
class ResnetCompleteNetworkTest (line 244) | class ResnetCompleteNetworkTest(tf.test.TestCase):
method _resnet_small (line 247) | def _resnet_small(self,
method testClassificationEndPoints (line 274) | def testClassificationEndPoints(self):
method testClassificationShapes (line 289) | def testClassificationShapes(self):
method testFullyConvolutionalEndpointShapes (line 306) | def testFullyConvolutionalEndpointShapes(self):
method testRootlessFullyConvolutionalEndpointShapes (line 324) | def testRootlessFullyConvolutionalEndpointShapes(self):
method testAtrousFullyConvolutionalEndpointShapes (line 343) | def testAtrousFullyConvolutionalEndpointShapes(self):
method testAtrousFullyConvolutionalValues (line 364) | def testAtrousFullyConvolutionalValues(self):
method testUnknownBatchSize (line 393) | def testUnknownBatchSize(self):
method testFullyConvolutionalUnknownHeightWidth (line 413) | def testFullyConvolutionalUnknownHeightWidth(self):
method testAtrousFullyConvolutionalUnknownHeightWidth (line 429) | def testAtrousFullyConvolutionalUnknownHeightWidth(self):
FILE: nets/vgg.py
function vgg_arg_scope (line 49) | def vgg_arg_scope(weight_decay=0.0005):
function vgg_a (line 66) | def vgg_a(inputs,
function vgg_16 (line 132) | def vgg_16(inputs,
function vgg_19 (line 198) | def vgg_19(inputs,
FILE: nets/vgg_test.py
class VGGATest (line 27) | class VGGATest(tf.test.TestCase):
method testBuild (line 29) | def testBuild(self):
method testFullyConvolutional (line 40) | def testFullyConvolutional(self):
method testEndPoints (line 51) | def testEndPoints(self):
method testModelVariables (line 77) | def testModelVariables(self):
method testEvaluation (line 110) | def testEvaluation(self):
method testTrainEvalWithReuse (line 122) | def testTrainEvalWithReuse(self):
method testForward (line 145) | def testForward(self):
class VGG16Test (line 156) | class VGG16Test(tf.test.TestCase):
method testBuild (line 158) | def testBuild(self):
method testFullyConvolutional (line 169) | def testFullyConvolutional(self):
method testEndPoints (line 180) | def testEndPoints(self):
method testModelVariables (line 211) | def testModelVariables(self):
method testEvaluation (line 254) | def testEvaluation(self):
method testTrainEvalWithReuse (line 266) | def testTrainEvalWithReuse(self):
method testForward (line 289) | def testForward(self):
class VGG19Test (line 300) | class VGG19Test(tf.test.TestCase):
method testBuild (line 302) | def testBuild(self):
method testFullyConvolutional (line 313) | def testFullyConvolutional(self):
method testEndPoints (line 324) | def testEndPoints(self):
method testModelVariables (line 359) | def testModelVariables(self):
method testEvaluation (line 409) | def testEvaluation(self):
method testTrainEvalWithReuse (line 421) | def testTrainEvalWithReuse(self):
method testForward (line 444) | def testForward(self):
Condensed preview — 40 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (671K chars).
[
{
"path": "LICENSE",
"chars": 11343,
"preview": "\n Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 3505,
"preview": "# Translation-Invariant Attacks\n\n## Introduction\nThis repository contains the code for [Evading Defenses to Transferable"
},
{
"path": "attack_iter.py",
"chars": 9423,
"preview": "\"\"\"Implementation of sample attack.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __fu"
},
{
"path": "attack_vgg16_mim.py",
"chars": 6293,
"preview": "\"\"\"Implementation of sample attack.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __fu"
},
{
"path": "dataset/README.md",
"chars": 2404,
"preview": "\n# Dataset for adversarial competition.\n\nTwo datasets will be used for the competition:\n\n* **DEV** dataset which is avai"
},
{
"path": "dataset/dev_dataset.csv",
"chars": 266125,
"preview": "ImageId,URL,x1,y1,x2,y2,TrueLabel,TargetClass,OriginalLandingURL,License,Author,AuthorProfileURL\r\n0c7ac4a8c9dfa802,https"
},
{
"path": "dataset/download_images.py",
"chars": 3544,
"preview": "\"\"\"Script which downloads dataset images.\n\nUsage:\n python download_images.py --input_file=INPUT_FILE --output_dir=IMAGE"
},
{
"path": "metadata.json",
"chars": 157,
"preview": "{\n \"type\": \"attack\",\n \"container\": \"tensorflow/tensorflow:1.6.0\",\n \"container_gpu\": \"tensorflow/tensorflow:1.6.0-gpu\""
},
{
"path": "nets/__init__.py",
"chars": 1,
"preview": "\n"
},
{
"path": "nets/alexnet.py",
"chars": 5357,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/alexnet_test.py",
"chars": 5819,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/cifarnet.py",
"chars": 4441,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception.py",
"chars": 1676,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_resnet_v2.py",
"chars": 16206,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_resnet_v2_test.py",
"chars": 11981,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_utils.py",
"chars": 2630,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v1.py",
"chars": 15434,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v1_test.py",
"chars": 8721,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v2.py",
"chars": 25032,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v2_test.py",
"chars": 13278,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v3.py",
"chars": 27427,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v3_test.py",
"chars": 12165,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v4.py",
"chars": 15678,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/inception_v4_test.py",
"chars": 9938,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/lenet.py",
"chars": 3499,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/mobilenet_v1.md",
"chars": 3961,
"preview": "# MobileNet_v1\n\n[MobileNets](https://arxiv.org/abs/1704.04861) are small, low-latency, low-power models parameterized to"
},
{
"path": "nets/mobilenet_v1.py",
"chars": 19467,
"preview": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/mobilenet_v1_test.py",
"chars": 22562,
"preview": "# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/nets_factory.py",
"chars": 5146,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/nets_factory_test.py",
"chars": 2299,
"preview": "# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# "
},
{
"path": "nets/overfeat.py",
"chars": 5206,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/overfeat_test.py",
"chars": 5753,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/resnet_utils.py",
"chars": 10449,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/resnet_v1.py",
"chars": 15274,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/resnet_v1_test.py",
"chars": 18981,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/resnet_v2.py",
"chars": 15334,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/resnet_v2_test.py",
"chars": 19106,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/vgg.py",
"chars": 11953,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "nets/vgg_test.py",
"chars": 18171,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "run_attack.sh",
"chars": 852,
"preview": "#!/bin/bash\n#\n# run_attack.sh is a script which executes the attack\n#\n# Envoronment which runs attacks and defences call"
}
]
About this extraction
This page contains the full source code of the dongyp13/Translation-Invariant-Attacks GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 40 files (641.2 KB), approximately 207.7k tokens, and a symbol index with 250 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.