Perceptual matching is used to sort categories of images. A quick DuckDuckGo will turn up many results. No stretch of the imagination will turn this into a bit for a bit comparison. This is a machine learning algorithm used to categorize images. https://www.ibm.com/blogs/research/2019/10/learning-implicit...

Matthew Green is tweeting about this: https://twitter.com/matthew_d_green/status/14230711866160005... and mentions that it is "preceptial hashing”

9 to 5 Mac article, in which they restate that it is not a classical bit-by-bit hash:

https://9to5mac.com/2021/08/05/scanning-for-child-abuse-imag...

“Perceptual hashing is the use of an algorithm that produces a snippet or fingerprint of various forms of multimedia.[1][2] A perceptual hash is a type of locality-sensitive hash, which is analogous if features of the multimedia are similar.”

https://en.wikipedia.org/wiki/Perceptual_hashing

The goal is to verify a black and white copy of an image is identical to a colored original. Search algorithms want a similar thing so they can validate an image contains a blue car. However, a perceptual hashing algorithm must differentiate between different images containing a blue car while matching a photoshopped copy of the same image.