There are advantages and disadvantages to both robust and cryptographic hash methods. Integrating the qualities of robustness and cryptographic confidentiality would be highly desirable. However, the challenge is that the concept of similarity is not applicable to cryptographic hashes, preventing direct comparison between robust and cryptographic hashes. Therefore, when incorporating robust hashes into cryptographic hashes, it becomes essential to develop methods that effectively capture the intrinsic properties of robust hashes without compromising their robustness. In order to accomplish this, it is necessary to anticipate the hash bits that are most susceptible to modification, such as those that are affected by JPEG compression. Our work demonstrates that the prediction accuracy of existing approaches can be significantly improved by using a new hybrid hash comparison strategy.
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.