# How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?

I am trying to implement a paper on Image tempering detection and localization, the paper is Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks, I was able to implement the SCNN, the one surrounded by red dots, I could not quite understand the FCNN, the one that is surrounded with blue dots.

The problem I am facing is: How the network made features vector from (1048 x 100) to (523 x 100) through max-pooling (instead of 524 x 100), and from (523 x 100) to (260 x 100) and then (260 x 100) to (256, ).

It appears that the given network diagram might be wrong, but, if it is wrong, how could it be published in IEEE. Please, help me understand how the FCNN is constructed.

• How did you get 520x100? With a max pooling filter of 2x1 I would assume the output should be 524x100 Jul 20 '20 at 4:26
• @Recessive sorry, typo it is 524 x 100 Jul 20 '20 at 4:34
• Ok that makes sense, at least the clarifies. Unfortunately I don't know why their numbers are off by one. I can only assume that they shifted the max pool filter by 1 at some point, but I can't know for sure or why. Hopefully someone else does Jul 20 '20 at 4:42