Abstract
In 2014 a novel identity theft scheme targeting specific application scenarios in face biometrics was introduced. In this scheme, a so called face morph melts two or more face images of different persons into one image, which is visually similar to multiple real world persons. Based on this non authentic image, it is possible to apply for an image based identity document to be issued by a corresponding authority. Thus, multiple persons can use such a document to pass image based person verification scenarios with a single document containing an artificially weakened template. Currently there is no reliable existing security mechanism to detect this attack.
This paper introduces a novel detection approach for face morphing forgeries based on a continuous image degradation. This is considered relevant because the degradation approach creates multiple artificial self-references and measures the “distance” from these references to the input. A small distance (significantly smaller than the one to be expected from a pristine image) could be considered as an anomaly here, indicating media manipulations (e.g. caused by morphing). Our degradation process is based on JPEG compression with different compression values. The evaluation results of our detection approach are classification accuracies of 90.1% under laboratory conditions and 84.3% under real world conditions.
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Acknowledgments
The work in this paper has been funded in part by the German Federal Ministry of Education and Research (BMBF) through the research programme ANANAS under the contract no. FKZ: 16KIS0509K. The author would like to thank Jana Dittmann and Andrey Makrushin for the initial ideas as well as the joint work with both of them and Christian Kraetzer for discussions on the approach evaluated in this paper.
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Neubert, T. (2017). Face Morphing Detection: An Approach Based on Image Degradation Analysis. In: Kraetzer, C., Shi, YQ., Dittmann, J., Kim, H. (eds) Digital Forensics and Watermarking. IWDW 2017. Lecture Notes in Computer Science(), vol 10431. Springer, Cham. https://doi.org/10.1007/978-3-319-64185-0_8
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