#Distillation as a Defense to Adversarial Perturbations #against Deep Neural Networks #Haobei Song, Mahesh Tripunitara(13 March 2017)

##There are three CNN training and testing scripts with the same architecture #InputLayer #Relu Convolutional 32 3X3 filters #Relu Convolutional 32 3X3 filters #Max Pooling 2X2 #Relu Convolutional 64 3X3 filters #Relu Convolutional 64 3X3 filters #Max Pooling 2X2 #Relu Fully Connect 200 units #Relu Fully Connect 200 units #Softmax 10 units

#Learing Rate 0.1 #Momentum 0.5 #Dropuout Rate (Fully Connect) 0.5 #Batch size 128 #Epochs (Default:50) #Temperature (Default:20) #Wrong test sample numbers per batch (Default:0)

#The standard CNN with Temperature at 1 ./std_CNN.py #The pre-training with Epochs=E, Sample Number=S, Temperature at T is called as ./pre_CNN.py E S T #The post-training is called the same as above ./post_CNN.py E S T

#the test samples for wrong tests for std_CNN are stored in saved_pics_old #the test samples for wrong tests for pre_CNN are stored in saved_pics_init #the test samples for wrong tests for post_CNN are stored in saved_pics

#The distilled labels are produced automatically after the call to pre_CNN.py and the resulting file is saved as distilled_labels.npz

#Reference: #1.Lasagne Documentation #2.N.Papernot, P.McDaniel, et al, "Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks," in Symposium on Security & Privacy, IEEE 2016. San Jose, CA.