He has been a member of the anti-piracy workgroup of the German Publishers and Booksellers Association since 2005, and since 2006 has held the position of spokesperson of the “Steganography and Watermarks” workgroup, which is a subgroup of the “Security” special interest group of the “Gesellschaft für Informatik e.V.”. Since 2011 he has been a member of the BITKOM working group “E-Books and Digital Publishing”, which deals with the subject of piracy protection.

Dr. Steinebach passes on the research results produced by the SIT in a variety of ways, such as during lectures, seminars and placements at the TU Darmstadt and the Darmstadt University of Applied Science and in numerous national and international publications and conferences.  He is also author and co-author of around 140 publications and 10 patent specifications on the subject of media security.

For this purpose, the known (illegal) images (see A in image above) are reduced to 16 x 16 pixel grey-scale images during the first step. These simplified image versions are then used to calculate the so-called robust hash value, which relates to a pixel matrix in which each pixel is displayed in white or black based on its brightness  (see B in image above). Newly confiscated image data is processed in the same way, in order to calculate the respective robust hash values.

Following this, the values are compared with the hash values of illegal images already stored on the database of the police. If the values of two images are the same in a sufficient number of areas, it can be presumed that the images are identical.

Robust hashing combines the benefits of the previous methods: it is fast, resistant against format conversions, changes in size and mirroring and has an extremely low error rate of just 1.1 false reports per 1000 images. It can be used as an addition or alternative to traditional procedures.

This value can be used to uniquely identify the underlying data, without enabling any clues regarding the original content to be derived. This is similar to a fingerprint, which can be used to identify a specific person in a very precise manner, without revealing what the person actually looks like.

Unlike robust hash values (see above), even very small changes to the original data result in a complete change to the hash value. For example, the changing of just a few individual letters within a text, or the cropping of an image, will lead to an entirely new value.

Hash values are used, for example, to generate digital signatures as well as in numerous encryption procedures. What’s more, they can also help to manage large volumes of data in a database efficiently or be used as checksums for identifying transmission errors.