Low Visual Distortion and Robust Morphing Attacks Based on Partial Face Image Manipulation

被引：21
作者：
Qin L. [1 ]
Peng F. [1 ]
Venkatesh S. [2 ]
Ramachandra R. [2 ]
Long M. [3 ]
Busch C. [2 ]
机构：
[1] College of Computer Science and Electronic Engineering, Hunan University, Changsha
[2] Norwegian Biometrics Laboratory, Norwegian University of Science and Technology, Gjovik
[3] College of Computer Science and Electronic Engineering, Hunan University, Changsha
来源：
IEEE Transactions on Biometrics, Behavior, and Identity Science | 2021年 / 3卷 / 01期
关键词：
Access control; face authentication; face morphing attack; facial manipulation; morphing attack detection;
D O I：
10.1109/TBIOM.2020.3022007
中图分类号：
学科分类号：
摘要：
Face verification is a popular way for verifying identities in access control systems. In this work, a partial face manipulation-based morphing attack (MA) is proposed to compromise the uniqueness of face templates. Different from existing research, this work changes MA from a holistic face level to component level, and only the most effective facial components (eyes and nose) are used. Therefore, a manipulated face is more similar to a bona fide one in terms of visual quality, texture, and noise characteristics. To validate the effectiveness of the proposed attack, a novel metric called actual mated morph presentation match rate (AMPMR) is proposed to evaluate MA performance under real-world conditions. With a collected dataset containing different attack types, image qualities, and manipulation parameters, the results indicate the proposed attack has better anti-detectability compared with the existing complete, splicing, and combined MAs. Moreover, it has low visual distortion and can reach a better tradeoff among facial biometrics verification, anti-detectability, and visual differences. © 2019 IEEE.
引用
收藏
页码：72 / 88
页数：16
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  • [1] Machine Readable Travel Documents, (2015)
  • [2] Ferrara M., Franco A., Maltoni D., The magic passport, Proc. IEEE Int. Joint Conf. Biometrics (IJCB), pp. 1-7, (2014)
  • [3] Makrushin A., Neubert T., Dittmann J., Automatic generation and detection of visually faultless facial morphs, Proc. 12th Int. Joint Conf. Comput. Vis. Imag. Comput. Graph. Theory Appl. (VISAPP), pp. 39-50, (2017)
  • [4] Neubert T., Makrushin A., Hildebrandt M., Kraetzer C., Dittmann J., Extended stirtrace benchmarking of biometric and forensic qualities of morphed face images, IET Biometrics, 7, 4, pp. 325-332, (2018)
  • [5] Damer N., Saladie A.M., Braun A., Kuijper A., MorGAN: Recognition vulnerability and attack detectability of face morphing attacks created by generative adversarial network, Proc. IEEE 9th Int. Conf. Biometrics Theory Appl. Syst. (BTAS), pp. 1-10, (2018)
  • [6] Damer N., Boutros F., Saladie A.M., Kirchbuchner F., Kuijper A., Realistic dreams: Cascaded enhancement of GAN-generated images with an example in face morphing attacks, Proc. IEEE 10th Int. Conf. Biometrics Theory Appl. Syst. (BTAS), pp. 1-10, (2019)
  • [7] Qian S., Et al., Make a face: Towards arbitrary high fidelity face manipulation, Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Seoul, South Korea, pp. 10033-10042, (2019)
  • [8] Makrushin A., Wolf A., An overview of recent advances in assessing and mitigating the face morphing attack, Proc. 26th Eur. Signal Process. Conf. (EUSIPCO), pp. 1017-1021, (2018)
  • [9] Scherhag U., Rathgeb C., Merkle J., Breithaupt R., Busch C., Face recognition systems under morphing attacks: A survey, IEEE Access, 7, pp. 23012-23026, (2019)
  • [10] Robertson D.J., Kramer R.S., Burton A.M., Fraudulent ID using face morphs: Experiments on human and automatic recognition, PLoS ONE, 12, 3, (2017)
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