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382 points DamonHD | 1 comments | | HN request time: 0.901s | source
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lynndotpy ◴[] No.43697899[source]
> Years ago it would've required a supercomputer and a PhD to do this stuff

This isn't actually true. You could do this 20 years ago on a consumer laptop, and you don't need the information you get for free from text moving under a filter either.

What you need is the ability to reproduce the conditions the image was generated and pixelated/blurred under. If the pixel radius only encompasses, say, 4 characters, then you only need to search for those 4 characters first. And then you can proceed to the next few characters represented under the next pixelated block.

You can think of pixelation as a bad hash which is very easy to find a preimage for.

No motion necessary. No AI necessary. No machine learning necessary.

The hard part is recreating the environment though, and AI just means you can skip having that effort and know-how.

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nartho ◴[] No.43698597[source]
Noob here, can you elaborate on this ? if you take for example a square of 25px and change the value of each individual pixels to the average color of the group, most of the data is lost, no ? if the group of pixels are big enough can you still undo it ?
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1. its-summertime ◴[] No.43698999[source]
How many pixels are in `I` vs how many are in `W`? different averages as a result. With subpixel rendering, kerning, etc, there is minute differences between the averages of `IW` and `WI` as well, order can be observed. It is almost completely based on having so much extra knowledge: the background of the text, the color of the text, the text renderer, the font, etc, there are a massive amount of unknowns if it is a random picture, but if we have all this extra knowledge, it massively cuts down on the amount of things we need to try: if its 4 characters, we can make a list of all possibilities, do the same mosaic, and find the closest match, in nearly no time.