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main.py
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main.py
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"""
Tensorflow (version = 1.4) implementation of the following algorithms from our paper ():
(*) Algorithms 1 and 2 (ours)
(*) Fast Gradient Sign from
(*) DeepFool from
These algorithms are benchmarked using the MNIST and CIFAR10 datasets in the following pre-trained models:
(*) FC-150-100-10 :
(*) LeNet-5 :
(*) Network-In-Network (NIN) :
(*) DenseNet :
File: main.py
Author: Emilio Balda <emilio.balda@ti.rwth-aachen.de>
Organization: RWTH Aachen University - Institute for Theoretical Information Technology
"""
import matplotlib
matplotlib.use('Agg')
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
import argparse
def get_arguments():
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
# ********************* DEFAULT INPUT VARIABLES (edit if necesary) *************************
model2load = 'fcnn'
models_dir = 'pretrainedmodels/'
output_dir = 'results/'
figs_dir = 'figures/'
data_dir = 'datasets/'
n_images = 128
max_epsilon = 0.2
# ********************* ******************************************* *************************
parser = argparse.ArgumentParser(description="Benchmarking of Algorithms for Generation of Adversarial Examples")
parser.add_argument("--rho-mode", help="Robustness Measure Mode: compute/store the robustness measures and exit",
action="store_true")
parser.add_argument("--model2load", type=str, default=model2load,
help="model to be loaded: either of these --> fcnn, lenet, nin, densenet. Default value = " + model2load)
parser.add_argument("--models-dir", type=str, default=models_dir,
help="Path to the directory containing the pre-trained model(s). Default value = " + models_dir)
parser.add_argument("--output-dir", type=str, default=output_dir,
help="Path to the directory where the output fooling ratio(s) will be stored. Default value = " + output_dir)
parser.add_argument("--figs-dir", type=str, default=figs_dir,
help="Path to the directory where the output figure(s) will be stored. Default value = " + figs_dir)
parser.add_argument("--data-dir", type=str, default=data_dir,
help="Path to the directory containing the dataset(s). Default value = " + data_dir)
parser.add_argument("--n-images", type=int, default=n_images,
help="Number of images of the dataset to be fooled. Default value = " + str(n_images))
parser.add_argument("--max-epsilon", type=float, default=max_epsilon,
help="Maximum epsilon value. It must be between 0 and 1. Default value = "+ str(max_epsilon))
return parser.parse_args()
def get_all_model_variables(args):
'''
Every model has its own names for the tensors in the grahp.
After looking at the graph of each model, in tensorboard using 'ViewGraph.py',
we create a dictionary with the tensors names of:
(*) 'input': The input image of the model
(*) 'logits': The output vector of the last layer of the model (before softmax is applied).
(*) 'pkeep': Dropout probability if applicable, None otherwise
'''
if (args.model2load == 'fcnn'):
modelvarnames = {
'model2load':args.model2load,
'models_dir':args.models_dir,
'output_dir':args.output_dir,
'figs_dir':args.figs_dir,
'data_dir':args.data_dir,
'n_images':args.n_images,
'max_epsilon':args.max_epsilon,
'graph_directory':'savedmodel_fcnn_mnist/',
'graph_file':'fcnn.ckpt.meta',
'input':'x:0',
'logits':'logits:0',
'pkeep':None
}
elif (args.model2load == 'lenet'):
modelvarnames = {
'model2load':args.model2load,
'models_dir':args.models_dir,
'output_dir':args.output_dir,
'figs_dir':args.figs_dir,
'data_dir':args.data_dir,
'n_images':args.n_images,
'max_epsilon':args.max_epsilon,
'graph_directory':'savedmodel_lenet_mnist/',
'graph_file':'lenet.ckpt.meta',
'input':'x:0',
'logits':'logits:0',
'pkeep':None
}
elif args.model2load == 'nin':
modelvarnames = {
'model2load':args.model2load,
'models_dir':args.models_dir,
'output_dir':args.output_dir,
'figs_dir':args.figs_dir,
'data_dir':args.data_dir,
'n_images':args.n_images,
'max_epsilon':args.max_epsilon,
'graph_directory':'savedmodel_nin_cifar/',
'graph_file':'nin.ckpt.meta',
'input':'x:0',
'logits':'logits:0',
'pkeep':'pkeep:0'
}
elif args.model2load == 'densenet':
modelvarnames = {
'model2load':args.model2load,
'models_dir':args.models_dir,
'output_dir':args.output_dir,
'figs_dir':args.figs_dir,
'data_dir':args.data_dir,
'n_images':args.n_images,
'max_epsilon':args.max_epsilon,
'graph_directory':'savedmodel_densenet_cifar/',
'graph_file':'graph-0301-145141.meta',
'input':'input:0',
'logits':'InferenceTower/linear/output:0',
'pkeep':None
}
else:
print('Error: Select a valid model (fcnn, lenet, nin, densenet)')
modelvarnames = None
return modelvarnames
def pre_process_data(X, y, model2load):
if model2load == 'fcnn' or model2load == 'lenet':
X = np.reshape(X, [X.shape[0], int(np.sqrt(X.shape[1])), int(np.sqrt(X.shape[1])),1])
X = np.pad(X, ((0,0),(2,2),(2,2),(0,0)), 'constant')
if model2load == 'nin':
mean = np.array([125.307, 122.95, 113.865]) / 255.
std = np.array([62.9932, 62.0887, 66.7048]) / 255.
for i in range(3):
X[:,:,:,i] = (X[:,:,:,i] - mean[i]) / std[i]
if model2load == 'densenet':
mean = np.array([125.307, 122.95, 113.865]) / 255.
for i in range(3):
X[:, :, :, i] = (X[:, :, :, i] - mean[i])
X = 255.*X
X, y = shuffle(X, y)
return X, y
def predict_CNN(X, modelvarnames):
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config = config) as sess:
saver = tf.train.import_meta_graph(modelvarnames['models_dir'] +\
modelvarnames['graph_directory'] +\
modelvarnames['graph_file'])
saver.restore(sess, tf.train.latest_checkpoint(modelvarnames['models_dir'] +\
modelvarnames['graph_directory']))
graph = tf.get_default_graph()
Xinput = graph.get_tensor_by_name(modelvarnames['input'])
Ylogits = graph.get_tensor_by_name(modelvarnames['logits'])
if modelvarnames['pkeep'] == None:
pkeep = tf.placeholder(tf.int32, (None)) # Not used
else:
pkeep = graph.get_tensor_by_name(modelvarnames['pkeep'])
Ysoftmax = tf.nn.softmax(Ylogits)
predicted_labels = tf.argmax(Ylogits, 1)
confidence = tf.reduce_max(Ysoftmax, 1)
Yout, yconf = sess.run([predicted_labels, confidence], feed_dict={Xinput: X, pkeep:1.0})
return Yout, yconf
def collect_correctly_predicted_images(X, y, modelvarnames):
Ninputs = int(1.5*modelvarnames['n_images'])
X = X[:Ninputs,:,:,:]
y = y[:Ninputs]
y_pred, y_conf = predict_CNN(X[:Ninputs,:,:,:], modelvarnames)
print('test error = ' + str(100.*np.mean(y_pred != np.argmax(y[:Ninputs],1))) + '%')
X = X[y_pred ==np.argmax(y[:Ninputs],1),:,:,:]
y = y[y_pred ==np.argmax(y[:Ninputs],1)]
X = X[:modelvarnames['n_images'],:,:,:]
y = np.argmax(y[:modelvarnames['n_images']], 1)
return X, y
def FastGrad(dX, epsilon):
return -epsilon * np.sign(dX)
def get_adversarial_noise(X, ypred, epsilon, modelvarnames,
method='FGS', iterations=1):
if method=='rand':
return epsilon * np.sign(np.random.randn(X.shape[0], X.shape[1], X.shape[2], X.shape[3]))
N = np.zeros(X.shape)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config = config) as sess:
saver = tf.train.import_meta_graph(modelvarnames['models_dir'] +\
modelvarnames['graph_directory'] +\
modelvarnames['graph_file'])
saver.restore(sess, tf.train.latest_checkpoint(modelvarnames['models_dir'] +\
modelvarnames['graph_directory']))
graph = tf.get_default_graph()
Xtrue = graph.get_tensor_by_name(modelvarnames['input'])
Ylogits = graph.get_tensor_by_name(modelvarnames['logits'])
if modelvarnames['pkeep'] == None:
pkeep = tf.placeholder(tf.int32, (None)) # Not used
else:
pkeep = graph.get_tensor_by_name(modelvarnames['pkeep'])
Ytrue = tf.placeholder(tf.int32, (None))
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits,
labels=tf.one_hot(Ytrue,10))
grad_loss = tf.gradients(loss, Xtrue)[0]
grad = tf.stack([tf.gradients(tf.gather(Ylogits, 0, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 1, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 2, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 3, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 4, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 5, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 6, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 7, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 8, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 9, axis=1), Xtrue)[0]])
if method == 'Alg1':
dX_y = np.zeros(X.shape)
N = np.zeros(X.shape)
for i in range(X.shape[0]):
XX = X[i,:,:,:]
for n in range(iterations):
dX, fY = sess.run([grad, Ylogits], feed_dict={Xtrue: np.array([XX]), pkeep:1.0})
tmp_norm = float('Inf')
l = -1
for j in range(10):
tmp = (fY[:, ypred[i]] - fY[:, j]) - epsilon/iterations * np.abs(
dX[ypred[i], :, :, :, :] - dX[j, :, :, :, :]).sum()
if j != ypred[i] and tmp < tmp_norm:
tmp_norm = tmp
l = j
dX_y[i, :, :, :] = dX[ypred[i], :, :, :, :] - dX[l, :, :, :, :]
N[i, :, :, :] = N[i, :, :, :] + FastGrad(dX_y[i, :, :, :], epsilon/iterations)
XX = X[i,:,:,:] + N[i, :, :, :]
elif method == 'FGS':
dX_y = np.zeros(X.shape)
for i in range(X.shape[0]):
dX = sess.run(grad_loss, feed_dict={Xtrue: np.array([X[i, :, :, :]]), Ytrue: np.array([ypred[i]]), pkeep:1.0})
dX_y[i, :, :, :] = dX[0, :, :, :]
N = FastGrad(-dX_y, epsilon)
elif method == 'Alg2':
dX_y = np.zeros(X.shape)
for i in range(X.shape[0]):
dX = sess.run(grad, feed_dict={Xtrue: np.array([X[i, :, :, :]]), pkeep:1.0})
dX_y[i, :, :, :] = dX[ypred[i], :, :, :, :]
N = FastGrad(dX_y, epsilon)
return N
def get_foolratio(X, y, epsilon, modelvarnames,
method='FGS', iterations=1):
N = get_adversarial_noise(X, y, epsilon, modelvarnames, method=method, iterations=iterations)
y_adv, y_conf = predict_CNN(X + N, modelvarnames)
return np.mean(y != y_adv)
def get_deepfool_ellenorms(X, ypred, modelvarnames,
max_its = 50,
overshoot=0.02):
# Parameters taken from --> https://github.com/LTS4/universal/blob/master/python/deepfool.py
elle_norms = np.zeros(X.shape[0])
fooled_vec = np.zeros(X.shape[0])
iterations_vec = np.zeros(X.shape[0])
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config = config) as sess:
saver = tf.train.import_meta_graph(modelvarnames['models_dir'] +\
modelvarnames['graph_directory'] +\
modelvarnames['graph_file'])
saver.restore(sess, tf.train.latest_checkpoint(modelvarnames['models_dir'] +\
modelvarnames['graph_directory']))
graph = tf.get_default_graph()
Xtrue = graph.get_tensor_by_name(modelvarnames['input'])
Ylogits = graph.get_tensor_by_name(modelvarnames['logits'])
if modelvarnames['pkeep'] == None:
pkeep = tf.placeholder(tf.int32, (None)) # Not used
else:
pkeep = graph.get_tensor_by_name(modelvarnames['pkeep'])
predicted_label = tf.argmax(Ylogits, 1)
grad = tf.stack([tf.gradients(tf.gather(Ylogits, 0, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 1, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 2, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 3, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 4, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 5, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 6, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 7, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 8, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 9, axis=1), Xtrue)[0]])
for i in range(X.shape[0]):
X0 = np.array([X[i,:,:,:]])
N = np.zeros(X0.shape)
XX = X0 + N
for n in range(max_its):
dX, fY = sess.run([grad, Ylogits], feed_dict={Xtrue: XX, pkeep: 1.0})
tmp_norm = float('Inf')
l=-1
for j in range(10):
if j != ypred[i]:
tmp = np.abs(fY[:,ypred[i]] - fY[:,j])/np.abs(dX[ypred[i],:,:,:,:] - dX[j,:,:,:,:]).sum()
if tmp < tmp_norm:
tmp_norm = tmp
l = j
N = N + (1 + overshoot)*tmp_norm*np.sign( dX[l,:,:,:,:] - dX[ypred[i],:,:,:,:])
XX = X0 + N
ypred_i_n = sess.run(predicted_label, feed_dict={Xtrue: XX, pkeep: 1.0})
if ypred_i_n != ypred[i]:
fooled_vec[i] = 1
break
elle_norms[i] = np.max(np.abs(N).flatten())
iterations_vec[i] = n
return elle_norms, fooled_vec, iterations_vec
def get_robustness_metrics(X, ypred, elle_norms, fooled_vec, modelvarnames,
method='rho_1'):
N = np.zeros(X.shape)
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config=config) as sess:
saver = tf.train.import_meta_graph(modelvarnames['models_dir'] + \
modelvarnames['graph_directory'] + \
modelvarnames['graph_file'])
saver.restore(sess, tf.train.latest_checkpoint(modelvarnames['models_dir'] + \
modelvarnames['graph_directory']))
graph = tf.get_default_graph()
Xtrue = graph.get_tensor_by_name(modelvarnames['input'])
Ylogits = graph.get_tensor_by_name(modelvarnames['logits'])
if modelvarnames['pkeep'] == None:
pkeep = tf.placeholder(tf.int32, (None)) # Not used
else:
pkeep = graph.get_tensor_by_name(modelvarnames['pkeep'])
Ytrue = tf.placeholder(tf.int32, (None))
loss = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits,
labels=tf.one_hot(Ytrue, 10))
grad_loss = tf.gradients(loss, Xtrue)[0]
grad = tf.stack([tf.gradients(tf.gather(Ylogits, 0, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 1, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 2, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 3, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 4, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 5, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 6, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 7, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 8, axis=1), Xtrue)[0],
tf.gradients(tf.gather(Ylogits, 9, axis=1), Xtrue)[0]])
out = None
if method=='rho1':
rho_1 = np.zeros(X.shape[0])
for i in range(X.shape[0]):
if fooled_vec[i]:
rho_1[i] = elle_norms[i]/np.max(np.abs(X[i, :, :, :]).flatten())
else:
rho_1[i] = -1
rho_1 = rho_1[rho_1 >0]
out = np.mean(rho_1)
elif method == 'rho2':
rho_2 = np.zeros(X.shape[0])
for i in range(X.shape[0]):
dX, fY = sess.run([grad, Ylogits], feed_dict={Xtrue: np.array([X[i, :, :, :]]), pkeep: 1.0})
tmp_norm = float('Inf')
l = -1
for j in range(10):
if j!= ypred[i]:
tmp = (fY[:, ypred[i]] - fY[:, j])/np.sum( np.abs(dX[ypred[i], :, :, :, :] - dX[j, :, :, :, :]).flatten())
if tmp < tmp_norm:
tmp_norm = tmp
rho_2[i] = tmp_norm
out = np.mean(rho_2)
elif method == 'eps99':
epsvec = np.linspace(0, np.max(elle_norms[fooled_vec == 1]), X.shape[0])
for i in range(X.shape[0]):
if np.mean(np.array((elle_norms<epsvec[i]) * (fooled_vec==1))) > 0.99:
out = epsvec[i]
break
else:
print('Non-valid robustness metric selected....')
return out
def save_foolratio_fig(X, fname, fool_dict, legend=True):
epsilon = X[:,fool_dict['epsilon']]
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(epsilon, X[:, fool_dict['DeepFool']] * 100., '-ro', markersize=8)
ax.plot(epsilon, X[:, fool_dict['Alg1-10']] * 100., '--bx')
ax.plot(epsilon, X[:, fool_dict['Alg1-5']] * 100., '-.bx')
ax.plot(epsilon, X[:, fool_dict['Alg1']] * 100., '-bx', markersize=10)
ax.plot(epsilon, X[:, fool_dict['Alg2']] * 100., '-g*', markersize=10)
ax.plot(epsilon, X[:,fool_dict['FGS']]*100., '-ms')
ax.plot(epsilon, X[:, fool_dict['rand']] * 100., '-^k')
plt.xlabel('epsilon', fontsize=16)
plt.ylabel('Fooling Ratio (in %)', fontsize=16)
ax.grid()
plt.ylim((-5,105))
if legend==True:
ax.legend(['DeepFool', 'Alg1 (10 its)', 'Alg1 (5 its)', 'Alg1', 'Alg2', 'FastGrad', 'random'], fontsize=16)
ax.tick_params(axis='both', labelsize='large')
plt.savefig( fname, format='eps', dpi=500 )
def main():
args = get_arguments()
allvars = get_all_model_variables(args)
# Load Test Dataset
if (allvars['model2load'] == 'fcnn') or (allvars['model2load'] == 'lenet'):
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(allvars['data_dir'], one_hot=True)
X = mnist.test.images
y = mnist.test.labels
# Free Memory
mnist = None
if (allvars['model2load'] == 'nin') or (allvars['model2load'] == 'densenet'):
import cifar10
cifar10.data_path = allvars['data_dir']
cifar10.maybe_download_and_extract()
X, _, y = cifar10.load_test_data()
# Free Memory
cifar = None
X, y = pre_process_data(X, y, allvars['model2load'])
X, y = collect_correctly_predicted_images(X, y, allvars)
eps_rescale = np.max(np.abs( np.max(X.flatten()) - np.min(X.flatten()) ))
deepfool_norms, deepfooled_vec, deepfool_its = get_deepfool_ellenorms(X, y, allvars)
print('DeepFool fooling ratio: '+str(np.mean(deepfooled_vec)*100)+' %')
print('DeepFool mean epsilon: '+str(np.mean(deepfool_norms[deepfooled_vec==1])/eps_rescale))
print('DeepFool mean iterations: ' + str(np.mean(deepfool_its[deepfooled_vec == 1])))
if args.rho_mode:
print('Computing performance metrics...')
print()
rho_1 = get_robustness_metrics(X, y, deepfool_norms, deepfooled_vec, allvars, method='rho1')
rho_2 = get_robustness_metrics(X, y, deepfool_norms, deepfooled_vec, allvars, method='rho2')/eps_rescale
eps99 = get_robustness_metrics(X, y, deepfool_norms, deepfooled_vec, allvars, method='eps99')/eps_rescale
print('rho_1 = ' + str(rho_1))
print('rho_2 = ' + str(rho_2))
print('eps99 = ' + str(eps99))
np.savetxt(allvars['output_dir'] + 'robustness_' + allvars['model2load'] + '_' + str(allvars['n_images']) + \
'_' + str(int(allvars['max_epsilon'] * 1000)) + '.csv',
np.array([rho_1, rho_2, eps99]),
delimiter=";")
else:
print('Computing fooling ratios...')
print()
epsilon = np.array(np.linspace(0.001, allvars['max_epsilon'], 10))
fool_dict = {'epsilon': 0, 'FGS': 1, 'Alg1': 2, 'Alg2': 3, 'rand': 4, 'DeepFool': 5, 'Alg1-5':6, 'Alg1-10':7}
fool_mtx = np.zeros([len(epsilon), len(fool_dict)])
for i in range(len(epsilon)):
eps = epsilon[i]*eps_rescale
print(allvars['model2load'] + ': realization '+str(i+1)+'/'+str(len(epsilon))+'...' )
fool_mtx[i, fool_dict['epsilon']] = epsilon[i]
fool_mtx[i, fool_dict['FGS']] = get_foolratio(X, y, eps, allvars, method='FGS')
fool_mtx[i, fool_dict['Alg1']] = get_foolratio(X, y, eps, allvars, method='Alg1', iterations=1)
fool_mtx[i, fool_dict['Alg2']] = get_foolratio(X, y, eps, allvars, method='Alg2')
fool_mtx[i, fool_dict['rand']] = get_foolratio(X, y, eps, allvars, method='rand')
fool_mtx[i, fool_dict['DeepFool']] = np.mean(np.array((deepfool_norms<eps) * (deepfooled_vec==1)))
fool_mtx[i, fool_dict['Alg1-5']] = get_foolratio(X, y, eps, allvars, method='Alg1', iterations=5)
fool_mtx[i, fool_dict['Alg1-10']] = get_foolratio(X, y, eps, allvars, method='Alg1', iterations=10)
np.savetxt(allvars['output_dir']+'fool_summary_' + allvars['model2load'] +'_' +str(allvars['n_images'])+\
'_'+str(int(allvars['max_epsilon']*1000))+'.csv', fool_mtx, delimiter=";")
save_foolratio_fig(fool_mtx,
allvars['figs_dir'] + 'fig_'+ allvars['model2load'] +'_' +str(allvars['n_images'])+\
'_'+str(int(allvars['max_epsilon']*1000))+'.eps',
fool_dict, legend=True)
if __name__ == '__main__':
main()