Privacy-Enhanced Robust Image Hashing with Bloom Filters

Authors

DOI:

https://doi.org/10.13052/jcsm2245-1439.1014

Keywords:

robust hashing, perceptual hashing, hashing, fingerprinting, forensics, Privacy-preserving

Abstract

Robust image hashes are used to detect known illegal images, even after image processing. This is, for example, interesting for a forensic investigation, or for a company to protect their employees and customers by filtering content. The disadvantage of robust hashes is that they leak structural information of the pictures, which can lead to privacy issues. Our scientific contribution is to extend a robust image hash with privacy protection. We thus introduce and discuss such a privacy-preserving concept. The approach uses a probabilistic data structure -- known as Bloom filter -- to store robust image hashes. Bloom filter store elements by mapping hashes of each element to an internal data structure. We choose a cryptographic hash function to one-way encrypt and store elements. The privacy of the inserted elements is thus protected. We evaluate our implementation, and compare it to its underlying robust image hashing algorithm. Thereby, we show the cost with respect to error rates for introducing a privacy protection into robust hashing. Finally, we discuss our approach's results and usability, and suggest possible future improvements.

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Author Biographies

Uwe Breidenbach, Technische Universität Darmstadt, Germany

Uwe Breidenbach. In 2016 Uwe Breidenbach graduated with a BSc in Computer Science, and in 2020 with a MSc in IT-Security, as well as a MSc in Computer Science from Technische Universität Darmstadt, Germany. He wrote his bachelor and master thesis at the Fraunhofer Institute for Secure Information Technology, Darmstadt, Germany. In addition, the master thesis was written in cooperation with the Faculty of Engineering of Mahidol University, Thailand. His main interest of research and work include penetration testing, ethical hacking, network security, digital forensics, and privacy protection.

Martin Steinebach, Fraunhofer Institute for Secure Information Technology SIT, 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 his 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 ATHENE and represents IT Forensics and AI 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 of the Deutscher IT Sicherheitspreis, an award funded by Host Görtz.

Huajian Liu, Fraunhofer Institute for Secure Information Technology SIT, 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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Published

2021-03-22

How to Cite

1.
Breidenbach U, Steinebach M, Liu H. Privacy-Enhanced Robust Image Hashing with Bloom Filters. JCSANDM [Internet]. 2021 Mar. 22 [cited 2024 Nov. 27];10(1):97-132. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/5983

Issue

2021: Vol 10 Iss 1

Section

ARES 2020 Workshops

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