Let's say we have a captcha system that consists of a greyscale picture (of a part of a street or something akin to re-captcha), divided into 9 blocks, with 2 missing pieces.
You need to choose the appropriate missing pieces from over 15 possibilities to complete the picture.
The puzzle pieces have their edges processed with glitch treatment as well as they have additional morphs such as heavy jpeg compression, random affine transform, and blurred edges.
Every challenge picture is unique - pulled from a dataset of over 3 million images.
Is it possible for the neural network to reliably (above 50%) predict the missing pieces? Sometimes these are taken out of context and require human logic to estimate the correct piece.
The chance of selecting two answers in correct order is 1/15*1/14.