################
# Constant
################
################
# Functions
################
################
# main
################
if __name__ == '__main__':
with tf.Session() as sess:
# load original data
data = CIFAR10()
test = data.X_test
# load classifier and detectors
classifier = CIFARModel('../Carlini/models/cifar', sess)
classifier_dis = CIFARModel('../Carlini/models/cifar-distilled-100',
sess)
# load attack
att_org = np.load(sys.argv[1])
att_dis = np.load(sys.argv[2])
att_name = sys.argv[3]
groundtruth = data.y_test[:128]
# detection and prediction
apred = np.argmax(classifier.model.predict(att_org), axis=1)
def softmax(self, x):
x = x / self.tempreture
return np.exp(x - np.mean(x)) / np.sum(np.exp(x - np.mean(x)), axis=0)
def JSD(self, P, Q):
P = self.softmax(P)
Q = self.softmax(Q)
_P = P / norm(P, ord=1)
_Q = Q / norm(Q, ord=1)
_M = 0.5 * (_P + _Q)
return 0.5 * (entropy(_P, _M) + entropy(_Q, _M))
################
# Main
################
if __name__ == '__main__':
# init
cifar = CIFAR10()
detector1 = DetectorI(input_shape=cifar.IMG_SHAPE)
#detector2 = DetectorII(input_shape=cifar.IMG_SHAPE)
# classifier
detector1.train(cifar.X_train,
cifar.X_train,
cifar.X_test,
cifar.X_test,
save_path='models/cifar_detector1')
# autoencoder
#detector2.train(mnist_move.X_train, mnist_move.X_train, mnist_move.X_test, mnist_move.X_test, save_path='models/detector2_move')