Privacy and Robust Hashes

Privacy-Preserving Forensics for Image Re-Identification




privacy, robust hashing, hashing, fingerprinting, forensics


Within a forensic examination of a computer for illegal image content, robust hashing can be used to detect images even after they have been altered. Here the perceptible properties of an image are used to create the hash values.
Whether an image has the same content is determined by a distance function.
Cryptographic hash functions, on the other hand, create a unique bit-sensitive value. With these, no similarity measurement is possible, since only with exact agreement a picture is found. A minimal change in the image results in a completely different cryptographic hash value.
However, the robust hashes have an big disadvantage: hash values can reveal something about the structure of the picture. This results in a data protection leak.
The advantage of a cryptographic hash function is in turn that its values do not allow any conclusions about the structure of an image.
The aim of this work is to develop a procedure for which combines the advantages of both hashing functions.


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Author Biographies

Martin Steinebach, Fraunhofer SIT, Darmstadt, Germany

Martin Steinebach is the manager of the Media Security and IT Forensics division at Fraunhofer SIT. From 2003 to 2007 he was the manager of the Media Security in IT division at Fraunhofer IPSI. He studied computer science at the Technical University of Darmstadt and finished his diploma thesis on copyright protection for digital audio in 1999. In 2003 he received his PhD at the Technical University of Darmstadt for this work on digital audio watermarking. In 2016 he became honorary professor at the TU Darmstadt. He gives lectures on Multimedia Security as well as Civil Security. He is Principle Investigator at CRISP and represents IT Forensics and Big Data Security. Before he was Principle Investigator at CASED with the topics Multimedia Security and IT Forensics. In 2012 his work on robust image hashing for detection of child pornography reached the second rank “Deutscher IT Sicherheitspreis”, an award funded by Host Görtz.

Sebastian Lutz, Fraunhofer SIT, Darmstadt, Germany

Sebastian Lutz received his B.Sc. in business informatics at the University of Mannheim, Germany, in 2015. He specialized his knowledge in security related topics as part of a M.Sc. IT-Security study in 2016 and successfully completed his degree in 2019. A major research interest was on information security, robust hashing and digital forensics. He wrote his master’s thesis “Privacy and Robust Hashing” at Fraunhofer SIT (Darmstadt, Germany). Currently he is an employee at ITK-Engineering (Rülzheim, Germany) and works as a Cyber Security Engineer. His duties include working as a developer and creating security concepts in the automotive and medical sectors.

Huajian Liu, Fraunhofer SIT, Darmstadt, Germany

Huajian Liu received his B.S. and M.S. degrees in electronic engineering from Dalian University of Technology, China, in 1999 and 2002, respectively, and his Ph.D. degree in computer science from Technical University Darmstadt, Germany, in 2008. He is currently a senior research scientist at Fraunhofer Institute for Secure Information Technology (SIT). His major research interests include information security, digital watermarking, robust hashing and digital forensics.


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How to Cite

Steinebach M, Lutz S, Liu H. Privacy and Robust Hashes: Privacy-Preserving Forensics for Image Re-Identification. JCSANDM [Internet]. 2020 Jan. 25 [cited 2023 Mar. 30];9(1):111-40. Available from:



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