Abstract
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.
References
C. W. Adams. Legal issues pertaining to the development of digital
forensic tools. In 2008 Third International Workshop on Systematic
Approaches to Digital Forensic Engineering, pages 123–132, May 2008.
F. Armknecht and A. Dewald. Privacy-preserving email forensics.
Technical Report CS-2015-03, Department Informatik, 2015.
C. De Roover, C. De Vleeschouwer, F. Lefebvre, and B. Macq. Robust
video hashing based on radial projections of key frames. IEEE Transactions
on Signal processing, 53(10):4020–4037, 2005.
P. N. Druzhkov and V. D. Kustikova. A survey of deep learning methods
and software tools for image classification and object detection. Pattern
Recognition and Image Analysis, 26(1):9–15, 2016.
J. Fridrich and M. Goljan. Robust hash functions for digital watermarking.
In Proceedings International Conference on Information Technology:
Coding and Computing (Cat. No. PR00540), pages 178–183. IEEE,
S. Gong, M. Cristani, C. C. Loy, and T. M. Hospedales. The
re-identification challenge. In Person re-identification, pages 1–20.
Springer, 2014.
A. Haouzia and R. Noumeir. Methods for image authentication: a survey.
Multimedia tools and applications, 39(1):1–46, 2008.
S. Hou, T. Uehara, S. M. Yiu, L. C. K. Hui, and K. P. Chow. Privacy
preserving multiple keyword search for confidential investigation of
remote forensics. In 2011 Third International Conference on Multimedia
Information Networking and Security, pages 595–599, Nov. 2011.
P. Kamavisdar, S. Saluja, and S. Agrawal. A survey on image classification
approaches and techniques. International Journal of Advanced
Research in Computer and Communication Engineering, 2(1):1005–
, 2013.
J. Katz, A. J. Menezes, P. C. Van Oorschot, and S. A. Vanstone.
Handbook of applied cryptography. CRC press, 1996.
E. V. A. N. Klinger and D. A. V. I. D. Starkweather. phash-the open
source perceptual hash library. Technical report, accessed 2016-05-
[Online]. Available: http://www.phash.org/apps, 2010.
O. Koval, S. Voloshynovskiy, F. Beekhof, and T. Pun. Security analysis
of robust perceptual hashing. In Security, Forensics, Steganography, and
Watermarking of Multimedia Contents X, volume 6819, page 681–906.
International Society for Optics and Photonics, 2008.
H. Liu, M. Steinebach, R. Stein, and F. Mayer. Privacy preserving
forensics for jpeg images. Electronic Imaging, 2018(7):1–6, 2018.
F. Mayer and M. Steinebach. Forensic image inspection assisted by
deep learning. In Proceedings of the 12th International Conference on
Availability, Reliability and Security, ARES ’17, pages 53:1–53:9, New
York, NY, USA, 2017. ACM.
A. Neelima and K. M. Singh. A short survey on perceptual hash
function. ADBU Journal of Engineering Technology, 1, 2014.
A. Peter, T. Hartmann, S. Muller, and S. Katzenbeisser. Privacypreserving
architecture for forensic image recognition. pages 79–84, 12
M. Schneider and S.-F. Chang. A robust content based digital signature
for image authentication. In Proceedings of 3rd IEEE International
Conference on Image Processing, volume 3, pages 227–230. IEEE,
S. Srinivasan. Security and privacy in the computer forensics context. In
International Conference on Communication Technology, pages
–3, Nov 2006.
P. Stahlberg, G. Miklau, and B. Neil Levine. Threats to privacy in the
forensic analysis of database systems. In Proceedings of the 2007 ACM
SIGMOD International Conference on Management of Data, SIGMOD
’07, pages 91–102, New York, NY, USA, 2007. ACM.
M. Steinebach. Robust hashing for efficient forensic analysis of image
sets. In International Conference on Digital Forensics and Cyber Crime,
pages 180–187. Springer, 2011.
M. Steinebach, H. Liu, and Y. Yannikos. Forbild: Efficient robust image
hashing. In Media Watermarking, Security, and Forensics 2012, volume
, page 83030O. International Society for Optics and Photonics,
L. Weng and B. Preneel. Attacking some perceptual image hash algorithms.
In 2007 IEEE International Conference on Multimedia and
Expo, pages 879–882. IEEE, 2007.
B. Yang, F. Gu, and X. Niu. Block mean value based image perceptual
hashing. In 2006 International Conference on Intelligent Information
Hiding and Multimedia, pages 167–172. IEEE, 2006.