Is such a captcha AI-resistant? 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.
Well to give you a short answer, I would say that YES, it would be MORE resistant than a more standard captcha approach...
This being said, I would still go as far as to predict something like a 75-80% successful prediction rates, for a custom model which is designed specifically for defeating a mechanism such as what you describe. The reason why I am fairly confident in such an appraisal, is primarily because of the following:

New techniques which researchers have begun to explore, which are intended to be &quot;Structure Preserving Convolutions&quot; which utilize a higher dimensional filter to store the extra correlation data.

I think that the obfuscation efforts that you mention will definitely help to some degree, although they can be easily defeated by training the model on a dataset which you pull out some portion of the samples during pre-processing and inject the same sort of noise and glitch treatments, etc.

An idea that would be worth exploring would be to process your dataset with an adversarial model which you could then use to generate Adversarial Noise that could then be fed into a pre-process step for your images and replace (or extend) the obfuscation efforts!




TL;DR: If you cant beat 'em, then join 'em! Just train a model to defeat your captcha implementation, and then use the model to generate adversarial examples and then apply obfuscations to your data set accordingly!
For more information on what I am talking about in my suggestion for further obfuscation efforts explore some of the papers you can find on Google Scholar - Ensemble Adversarial Training Examples