import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.metrics import roc_curve,auc from scipy import interp from itertools import cycle from utilities import read_images_steering_directions, generate_data, SDC_data IMAGE_FILE = 'straight_right_left.csv' IMAGE_FOLDER = '/home/alesia/Documents/sdc/' data = SDC_data(IMAGE_FILE, IMAGE_FOLDER) train_labels = np.asarray(data.attack_labels) NUM_ATTACKS = 600 NUM_CLASSES = 3 NUM_INPUTS = 300 LABELS_FILE = 'results/res_attack_labels_epoch.txt' PROBAS_FILE = 'results/res_attack_probas_epoch.txt' DISTANCES_FILE = 'results/res_attack_epoch.txt' shape = (NUM_ATTACKS, NUM_CLASSES)
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn, optimizer=sgd, metrics=['accuracy'])
model.fit(data.attack_data,
data.attack_labels,
batch_size=batch_size,
validation_split=0.1,
nb_epoch=num_epochs,
shuffle=True)
if file_name != None:
model.save(file_name)
return model
if not os.path.isdir('models'):
os.makedirs('models')
IMAGE_FILE = 'straight_right_left.csv'
IMAGE_FOLDER = '/home/alesia/Documents/sdc/'
train(SDC_data(IMAGE_FILE, IMAGE_FOLDER),
"models/sdc_nvidia", [24, 36, 48, 64, 64, 1164, 100, 50, 10, 3],
num_epochs=50)
from utilities import SDC_data, generate_data, softmax
from attack import L2ClassificationAttack
from model import SDC_model_epoch
IMAGE_FILE = 'straight_right_left.csv'
IMAGE_FOLDER = '/home/alesia/Documents/sdc/'
MODEL_FILE = 'models/sdc_epoch'
RESULTS_FILE = 'results/res_attack_epoch.txt'
RESULTS_SUCCESS_FILE = 'results/res_attack_success_epoch.txt'
RESULTS_PROBAS_FILE = 'results/res_attack_probas_epoch.txt'
RESULTS_LABELS_FILE = 'results/res_attack_labels_epoch.txt'
NUM_ATTACKS = 300
with tf.Session() as sess:
data, model = SDC_data(IMAGE_FILE,
IMAGE_FOLDER), SDC_model_epoch(MODEL_FILE, sess)
distortions = []
success = 0
results = open(RESULTS_FILE, 'w')
results_success = open(RESULTS_SUCCESS_FILE, 'w')
results_probas = open(RESULTS_PROBAS_FILE, 'w')
results_labels_probas = open(RESULTS_LABELS_FILE, 'w')
tmp = 0
for k in range(NUM_ATTACKS):
attack = L2ClassificationAttack(sess,
model,
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels = correct,
logits = predicted)
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn,
optimizer=sgd,
metrics=['accuracy'])
model.fit(data.attack_data, data.attack_labels,
batch_size=batch_size,
validation_split = 0.1,
nb_epoch=num_epochs,
shuffle=True)
if file_name != None:
model.save(file_name)
return model
if not os.path.isdir('models'):
os.makedirs('models')
IMAGE_FILE = 'straight_right_left.csv'
IMAGE_FOLDER = '/home/alesia/Documents/sdc/'
train(SDC_data(IMAGE_FILE, IMAGE_FOLDER), "models/sdc_epoch", [32, 64, 128, 1024, 3], num_epochs = 50)
from utilities import SDC_data, generate_data
from attack import L2ClassificationAttack
from model import SDC_model_nvidia
IMAGE_FILE = 'straight_right_left.csv'
IMAGE_FOLDER = '/home/alesia/Documents/sdc/'
MODEL_FILE = 'models/sdc_nvidia'
RESULTS_FILE = 'results/res_attack_nvidia.txt'
RESULTS_SUCCESS_FILE = 'results/res_attack_success_nvidia.txt'
RESULTS_PROBAS_FILE = 'results/res_attack_probas_nvidia.txt'
RESULTS_LABELS_FILE = 'results/res_attack_labels_nvidia.txt'
NUM_ATTACKS = 300
with tf.Session() as sess:
data, model = SDC_data(IMAGE_FILE,
IMAGE_FOLDER), SDC_model_nvidia(MODEL_FILE, sess)
distortions = []
success = 0
results = open(RESULTS_FILE, 'w')
results_success = open(RESULTS_SUCCESS_FILE, 'w')
results_probas = open(RESULTS_PROBAS_FILE, 'w')
results_labels = open(RESULTS_LABELS_FILE, 'w')
for k in range(NUM_ATTACKS):
attack = L2ClassificationAttack(sess,
model,
batch_size=2,
max_iterations=1000,
confidence=0)
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from keras.utils import np_utils
from keras.models import load_model
import matplotlib.pyplot as plt
from utilities import SDC_data, generate_data
from model import SDC_model_epoch
from attack import L2RegressionAttack
IMAGE_FILE = 'test_image.csv'
IMAGE_FOLDER = '/home/alesia/Documents/sdc/'
with tf.Session() as sess:
data, model = SDC_data(IMAGE_FILE, IMAGE_FOLDER), SDC_model_epoch("models/sdc", sess)
attack = L2RegressionAttack(sess, model, batch_size=1, max_iterations=1000, confidence=0)
inputs, targets = generate_data(data, samples=1, targeted=True, start=0)
adv = attack.attack(inputs, targets)
for i in range(len(adv)):
inp = model.model.predict(inputs[i:i+1])
print("Valid :",inp)
adver = model.model.predict(adv[i:i+1])
print("Classification:", adver)
print()