Correlations in the math domain get weird. It wouldn't surprise me in the slightest that those slight distortions you see in the "memorized" image versus the or

Correlations in the math domain get weird. It wouldn't surprise me in the slightest that those slight distortions you see in the "memorized" image versus the original ones, while small to humans, turn out to be staggeringly large to your perceptual hash. See the things like the little stickers you can stick to stop signs to make some neural nets decide they are people and such. And it could go the other way too; it could be that the perceptual hashes are even "better" than humans at seeing past those distortions. My point is that this all gets more complicated than you may think when you're trying to apply a hash designed for real images against the output of an algorithm. And even if the hash worked perfectly, the false positive and false negative landscape would still be very likely to contain very surprising things.