Images can be recognized by cryptographic or robust hashes during forensic investigation or content filtering. Cryptographic methods tend to be too fragile, robust methods may leak information about the hashed images. Combining robust and cryptographic methods can solve both problems, but requires a good prediction of hash bit positions most likely to break. Previous research shows the potential of this approach, but evaluation results still have rather high error rates, especially many false negatives. In this work we have a detailed look at the behavior of robust hashes under attacks and the potential of prediction derived from distance from median and learning from attacks.