Abstract

A Pseudo Random Number Generator (PRNG) uses a deterministic system and an initial seed to generate random numbers. In order for the output sequence to be truly random, a truly random input seed is used. Most True Random Number Generators (TRNG), use noise in the form nuclear decay, atmospheric noise, electrical noise or Brownian motion as their initial seed. In order to reduce the computational complexity, we use a simple setup of a fish tank as the variable environment, capturing its images over time. The image data is then applied to a reduction algorithm and hash function to generate the initial seed. We propose a cost efficient method of extracting the true seed from the image data and applying it to a pseudo random generator, a Linear Congruential Generator (LCG) in our case to give true random numbers.

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