Both robust and cryptographic hash methods have advantages and disadvantages. It would be ideal if robustness and cryptographic confidentiality could be combined. The problem here is that the concept of similarity of robust hashes cannot be applied to cryptographic hashes. Therefore, methods must be developed to reliably intercept the degrees of freedom of robust hashes before they are included in a cryptographic hash, but without losing their robustness. To achieve this, we need to predict the bits of a hash that are most likely to be modified, for example after a JPEG compression. We show that machine learning can be used to make a much more reliable prediction than the approaches previously discussed in the literature.