I guess this falls into a category that the ML algo learns a particular type very well and cannot recognise obvious cases if they look different than the training data. Human intelligence is a mix of pattern matching and attention focus that is hard to provide with a single pattern matching ML project. Isn't there projects try to use multiple pattern matching models combined to decrease the amount of false negatives?