Hashing, at its simplest, is turning an arbitrarily large chunk of data into a single hopefully unique value.

For example, if I wanted to hash a 4-letter word, the simple version would be as such:

` H A S H

` 8 1 19 8

If we take the numeric value of each, we can add those together and get 36. If the number gets too high, there would be a clamping mechanism to keep it manageable. For our simplistic example, we could chop off any hundreds place digits or higher. Now if I were to hash a different four-letter word, the odds of it having the same hash value (known as a “collision”) are low. Thus if you tell me you sent me a message with a hash of 36, I can look at the message you sent, calculate the hash, and confirm that it’s the same message you intended to send with a certain degree of confidence.

Now modern hashing is vastly more complicated (https://en.m.wikipedia.org/wiki/MD5), but the gist is the same. Take the data in a file, jam it all together through an algorithm to come up with a hash value, then use that to find equivalent files.

The problem here is that if it’s a classic data validation hash algorithm, changing just a single bit can change the entire hash, which would foil an identification system. So hopefully this system actually hashes images based on some kind of relative semantic information within the photo, such as color distributions and features so even if you crop or adjust the image slightly the hash still matches.