Binary neural network encryption quality I am creating a Binary Neural Network that encrypts payloads and I would like to compare it to AES in quality, for example: With BNN you would need XX years to decrypt where with AES would take YY years. How can I do that??
Currently, I have two tests:

A graphic representation that shows how random the payload is, like the image below:

The STS randomness test, which gives me the following numbers:


Are there any other test(s) that I can perform to increase the confidence that the encrypted payload is comparable/best/worst than AES??
What you are trying to do is fundamentally incompatible with cryptographic methods as would be described by this site.  You are looking at an analog input, and analog output, and a lossy compression.  Neural networks by definition have a stochastic behavior because the axon-&gt;dendrite interface is inherently unreliable.
There is a body of work in the encryption of analog signals with chaotic oscillators.  I would personally not call it encryption because there is loss, but the brain or machine learning can figure out the output.
The basis of this is Chua's Circuit, and if you look dig around in libraries you can find how these different compression methods were handled in the 90s during the analog to digital channel transistion.