Yeah I'm unclear on why this even requires a clever algorithm. I'd think that given the 1.4 million precomputed hashes, a simple/naive Python function (<10 line

Yeah I'm unclear on why this even requires a clever algorithm. I'd think that given the 1.4 million precomputed hashes, a simple/naive Python function (<10 lines) that builds a dict mapping hash -> (list of images with that hash) could surface all the collisions in a few seconds. (It's a cool article though! I'm glad someone tested this.) Edit: I'm procrastinating so I tried it. It's 8 lines including IO/parsing and runs in 2.8 seconds on my laptop: import collections hash_to_filenames = collections.defaultdict(list) with open('hashes.txt') as f: for line in f.readlines(): filename, hash = line.strip().split() hash_to_filenames[hash].append(filename) dupes = {h: fs for h, fs in hash_to_filenames.items() if len(fs) > 1} print(f'Done. Found {len(dupes)} dupes.') (hashes.txt is from the zip in the github repo, and it finds 8865 dupes which looks almost right from the article text (8272 + 595 = 8867).)