Abstract
In this paper, we introduce a concept to counter the current weakness of robust hashing with respect to cropping. We combine face detectors and robust hashing. By doing so, the detected faces become a subarea of the overall image which always can be found as long as cropping of the image does not remove the faces. As the face detection is prone to a drift effect altering size and position of the detected face, further mechanisms are needed for robust hashing. We show how face segmentation utilizing blob algorithms can be used to implement a face-based cropping robust hash algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Poisel, R., Tjoa, S.: Forensics investigations of multimedia data: a review of the state-of-the-art. In: 2011 Sixth International Conference on IT Security Incident Management and IT Forensics (IMF), pp. 48–61, May 2011
Schwarzer, G., Massaro, D.W.: Modeling face identification processing in children and adults. J. Exp. Child Psychol. 79(2), 139–161 (2001)
Quayle, E., Taylor, M., Holland, G.: Child pornography: the internet and offending. Isuma Can. J. Policy Res. 2(2), 94–100 (2001)
Yang, B., Gu, F., Niu, X.: Block mean value based image perceptual hashing. In: Proceedings of the International Conference on Intelligent Information Hiding and Multimedia Multimedia Signal Processing (IIH-MSP), pp. 167–172. IEEE (2006). (ISBN 0-7695-2745-0)
Zauner, C., Steinebach, M., Hermann, E.: Rihamark: perceptual image hash benchmarking. In: Proceeding of Electronic Imaging 2011 - Media Watermarking, Security, and Forensics XIII (2011)
Steinebach, M.: Robust hashing for efficient forensic analysis of image sets. In: Gladyshev, P., Rogers, M.K. (eds.) ICDF2C 2011. LNICST, vol. 88, pp. 180–187. Springer, Heidelberg (2012)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: IEEE ICIP 2002, pp. 900–903 (2002)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001, CVPR 2001, vol. 1, pp. I–511. IEEE (2001)
Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vis. 57(2), 137–154 (2001)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Otsu, N.: A threshold selection method from grey level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979). ISSN 1083-4419
Galda, H.: IPD - image processing design toolbox version 2.0, Scilab Toolbox (2009)
Steinebach, M., Liu, H., Yannikos, Y.: ForBild: Efficient robust image hashing. In: Media Watermarking, Security, and Forensics 2012, SPIE, Burlingame, California, United States (2012). ISBN,978-0-8194-8950-02012
Acknowledgments
This work has been supported by the projects ForSicht and CASED, both funded by the State of Hessen.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Steinebach, M., Liu, H., Yannikos, Y. (2014). FaceHash: Face Detection and Robust Hashing. In: Gladyshev, P., Marrington, A., Baggili, I. (eds) Digital Forensics and Cyber Crime. ICDF2C 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-319-14289-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-14289-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14288-3
Online ISBN: 978-3-319-14289-0
eBook Packages: Computer ScienceComputer Science (R0)