Exact-duplicate search misses a mirrored, cropped, or filtered copy of an image, so this post combines a perceptual hash and a CLIP embedding into one rule you control and shows the copies as a graph in FiftyOne.

Your model's test metrics look great. Then someone opens the test set and finds a mirrored copy of a training image. Then another one, with a filter on it. Then a third, cropped and covered in emoji.

You run a duplicate check. It finds nothing.

That's because duplicate checks look for the same bytes, and an edited copy has different bytes. A mirrored JPEG is a different file from the original. To a hash of the file contents, the two are strangers. To a person, they're the same picture.

The task is called image copy detection . Given a query image, decide whether it's an edited copy of an image in a reference collection, and say which one.…