Survey on Different Morphology Detection Techniques
DOI:
https://doi.org//10.32628/IJSRST52310414Keywords:
Morph Attack detection, Artificial Neural NetworkAbstract
There is a lot of data traffic that translates and transports data in the digital environment of the World Wide Web. The data is presented as files and photos. Data can morph, so it's important to detect these instances. The suggested method will identify modified photographs and alert the user to the validity of the images. In recent years, the research community has paid a great deal of attention to the problem of morph attack detection. To accurately detect morph attacks, various studies have been done in this area and various methods have been used. Although enough morph images are not readily available for research purposes, a variety of face databases are used to create morph image databases. Attack detection via face morphing is difficult. Automated border control gates utilize both manual inspection and automatic categorization methods to identify morphing attacks. It is vital to comprehend how a machine learning system can recognise altered faces and the most pertinent facial regions.
References
- G. Borghi, A. Franco, G. Graffieti and D. Maltoni, "Automated Artifact Retouching in Morphed Images With Attention Maps," in IEEE Access, vol. 9, pp. 136561-136579, 2021, doi: 10.1109/ACCESS.2021.3117718.
- M. Hamza, S. Tehsin, H. Karamti and N. S. Alghamdi, "Generation and Detection of Face Morphing Attacks," in IEEE Access, vol. 10, pp. 72557-72576, 2022, doi: 10.1109/ACCESS.2022.3188668.
- R. Raghavendra and G. Li, "Multimodality for Reliable Single Image Based Face Morphing Attack Detection," in IEEE Access, vol. 10, pp. 82418-82433, 2022, doi: 10.1109/ACCESS.2022.3196773.
- J. E. Tapia and C. Busch, "Single Morphing Attack Detection Using Feature Selection and Visualization Based on Mutual Information," in IEEE Access, vol. 9, pp. 167628-167641, 2021, doi: 10.1109/ACCESS.2021.3136485.
- S. Zhu, "Edge detection based on multi-structure elements morphology and image fusion," 2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering, Wuhan, China, 2011, pp. 406-409, doi: 10.1109/CCIENG.2011.6008150.
- K. He, C. M. Ferrara, A. Franco, and D. Maltoni, ‘‘The magic passport,’’ in Proc. IEEE Int. Joint Conf. Biometrics, Sep. 2014, pp. 1–7.
- U. Scherhag, C. Rathgeb, J. Merkle, R. Breithaupt, and C. Busch, ‘‘Face recognition systems under morphing attacks: A survey,’’ IEEE Access, vol. 7, pp. 23012–23026, 2019.
- U. Scherhag, C. Rathgeb, J. Merkle, and C. Busch, ‘‘Deep face representations for differential morphing attack detection,’’ IEEE Trans. Inf. Forensics Security, vol. 15, pp. 3625–3639, 2020.
- S. Venkatesh, R. Ramachandra, K. Raja, L. Spreeuwers, R. Veldhuis, and C. Busch, ‘‘Morphed face detection based on deep color residual noise,’’ in Proc. 9th Int. Conf. Image Process. Theory, Tools Appl. (IPTA), Nov. 2019, pp. 1–6.
- S. Venkatesh, R. Ramachandra, K. Raja, and C. Busch, ‘‘Face morphing attack generation & detection: A comprehensive survey,’’ IEEE Trans. Technol. Soc., vol. 2, no. 3, pp. 128–145, Sep. 2020.
- M. Ngan, P. J. Grother, K. K. Hanaoka, and J. Kuo, Face Recognition Vendor Test (FRVT) Part 4: Morph-Performance of Automated Face Morph Detection. Gaithersburg, MD, USA: National Institute of Technology, 2020.
- R. Raghavendra, K. B. Raja, and C. Busch, ‘‘Detecting morphed face images,’’ in Proc. IEEE 8th Int. Conf. Biometrics Theory, Appl. Syst. (BTAS), Sep. 2016, pp. 1–7.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST
This work is licensed under a Creative Commons Attribution 4.0 International License.
