how does the lasagne implementation of the papernot cnn not run out of input volume?

[paullintilhac]

Given this back of the envelope calculation, it seems like the input volume should be smaller than the kernel size at the third convolutional layer:

model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28,28, 1)))
model.add(Conv2D(32, (5, 5)))
#after this we should have input volume of size 24x24
model.add(Activation('relu'))
model.add(Conv2D(32, (5, 5)))
#20x20
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#10x10
model.add(Conv2D(64, (5, 5)))
#6x6
model.add(Activation('relu'))
model.add(Conv2D(64, (5, 5)))
#1x1 
model.add(Activation('relu'))
##we should get an error here becuse there isn't enough input volume to do a 2x2 maxpoolinglayer
model.add(MaxPooling2D(pool_size=(2, 2))) 

So the question is... why don't we run into this issue with the lasagne implementation by Bhagoji?