International Journal of Computer & Organization Trends  (IJCOT)          
© 2021 by IJCOT Journal
Volume - 11 Issue - 4
Year of Publication : 2021
Authors : V. Muthuvel Vijai, P.A. Mathina
DOI :  10.14445/22492593/IJCOT-V11I4P302


MLA Style:V. Muthuvel Vijai, P.A. Mathina."An Effective Ring Partition And Half toning Combined Face Morphing Detection" International Journal of Computer and Organization Trends 11.4 (2021): 10-14. 

APA Style:V. Muthuvel Vijai, P.A. Mathina.(2021) An Effective Ring Partition And Half toning Combined Face Morphing Detection International Journal of Computer and Organization Trends, 11(4), 10-14.


Because of the advances in PC-based correspondence and wellbeing administrations over the previous decade, the requirement for picture security gets earnest to address the prerequisites of both wellbeing and non-security in all applications. Strategies for confirmation and self-recuperation of altered data in computerized pictures have been in steady advancement during the last years. This undertaking proposes another ring part-based half conditioning plan for picture validation and self-recuperation for picture applications. The proposed framework incorporates ring segment with Gray level co-event network (GLCM), a model-based saliency identification, and a visually impaired mathematical revision. To begin with, the worldwide highlights are separated dependent on GLCM from pivots invariant districts, i.e., through ring parcels. Then, the nearby highlights are extricated utilizing a model-based saliency identification technique. The two highlights are connected to frame the last hash. At the hour of picture confirmation, the mathematical changes are moderated by means of a visually impaired mathematical change rectification approach. The proposed half conditioning strategy was coded in Verilog and carried out in SPARTAN to show lower intricacy and low force picture handling capacity of the proposed structure. To assess the nature of the got pictures, the target model of pinnacle signal-to-clamor proportion (PSNR) and Tampering proportion are utilized. The exploratory outcomes show the viability of our technique in examinations with different plans announced in writing, where the nature of the watermarked pictures, the nature of the remaking pictures, and the recuperation pace of each plan was assessed.


[1] Debiasi, L., Rathgeb, C., Scherhag, U., Uhl, A., & Busch, C., PRNU Variance Analysis for Morphed Face Image Detection. 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)., (2018).

[2] Galea, C., & Farrugia, R. A., Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning. IEEE Transactions on Information Forensics and Security, 13(6) (2018) 1421–1431.

[3] Hildebrandt, M., Neubert, T., Makrushin, A., & Dittmann, J., Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps. 2017 5th International Workshop on Biometrics and Forensics (IWBF)., (2017).

[4] Raghavendra, R., Raja, K., Venkatesh, S., & Busch, C., Face morphing versus face averaging: Vulnerability and detection. 2017 IEEE International Joint Conference on Biometrics (IJCB).,. (2017).

[5] Venkatesh, S., Ramachandra, R., Raja, K., & Busch, C., Single Image Face Morphing Attack Detection Using Ensemble of Features. 2020 IEEE 23rd International Conference on Information Fusion (FUSION)., (2020).

[6] Scherhag, U., Debiasi, L., Rathgeb, C., Busch, C., & Uhl, A., Detection of Face Morphing Attacks based on PRNU Analysis. IEEE Transactions on Biometrics, Behavior, and Identity Science, (2019) 1–1.

[7] Peng, F., Zhang, L.-B., & Long, M., FD-GAN: Face De-Morphing Generative Adversarial Network for Restoring Accomplice’s Facial Image. IEEE Access, 7 (2019) 75122–75131.

[8] Neubert, T., Kraetzer, C., & Dittmann, J., Reducing the False Alarm Rate for Face Morph Detection by a Morph Pipeline Footprint Detector. 2018 26th European Signal Processing Conference (EUSIPCO)., (2018).

[9] Huang, Y., & Hu, H., A Parallel Architecture of Age Adversarial Convolutional Neural Network for Cross-Age Face Recognition. IEEE Transactions on Circuits and Systems for Video Technology, (2020) 1–1.

[10] S.Jagatheeswari, M. Malini, M.Nagapushpam, M.Vigneshwari , Mrs.V. Krishnameera, An Effective Halftoning Based Image Forgery And Morphing Detection, SSRG International Journal of Mobile Computing and Application 8(1) (2021) 11-16.


Ring Partition, Face Morphing, Half toning