Digital Image Processing Using YCbCr Colour Space and Neuro Fuzzy to Identify Pornography
DOI:
https://doi.org/10.15575/join.v8i1.1070Keywords:
Content, Digital image, Neuro-Fuzzy, Pornographic identification , YCbCrAbstract
Pornography is a severe problem in Indonesia, apart from drugs. This can be seen based on data from the Ministry of Communication and Informatics in 2021 which found 1.1 million pornographic content online. The increasing number of access to pornographic content sites on the internet can prove this. Several studies have been conducted to produce preventive formulas. However, this research flow has not been effective in solving the problem. This is because the results of the identification value in the output image obtained are not quite right. This study proposes a procedure for identifying pornographic content in digital images as an alternative approach for the early stages of a destructive content access prevention system. The formulation uses the YCbCr color space to analyze human skin on image objects that represent exposed body parts and the classification process with the Neuro Fuzzy approach. The performance of this formula was tested on 100 digital images of random categories of human objects (usually covered, skimpy, and naked) taken from the internet. The test results are at a relatively good level of accuracy, with a weight of 70% for the entire test data.
References
R. I. Undang-Undang, “No. 44 Tahun 2008 Tentang Pornografi,” Asa Mandiri, 2008.
M. E. Christanti, “The Relationship Between Pornography Exposure Through Mass Media and The Age of Menarche in Grade V and VI Students at Al Kautsar Elementary School in 2019/2020 Academic Year.” Poltekkes Tanjungkarang, 2020.
N. Maftuchah, “Use of Google Safe Browsing to Protect Yourself from Pornographic Sites on Social Media.”
J. Christiany, “Cybersex Behavior in the Millennial Generation,” J. Pekommas, vol. 5, no. 1, pp. 47–58, 2020.
N. K. Sa’diyah, “Inhibiting Factors in the Prevention and Handling of Cyberporn in the Cyber World in Efforts to Reform Criminal Law,” Perspektif, vol. 23, no. 2, pp. 94–106, 2018.
F. Sugiarto, M. N. Janhari, and H. Hotimah, “Interpretation of the Hijab in the Al-Qur’an Surah Al-Ahzab
Verse 59 According to Buya Hamka on Al-Azhar Interpretation,” Madinah J. Stud. Islam, vol. 7, no. 1, pp. 118–128, 2020.
K. Manaf, S. W. Pitara, B. Subaeki, and R. Gunawan, “Comparison of Carp Rabin Algorithm and Jaro-Winkler Distance to Determine The Equality of Sunda Languages,” in 2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA), 2019, pp. 77–81.
G. G. Yen and P. Meesad, “An effective neuro-fuzzy paradigm for machinery condition health monitoring,” IEEE Trans. Syst. Man, Cybern. Part B, vol. 31, no. 4, pp. 523–536, 2001.
S. Y. Mumtaj and A. Umamakeswari, “Neuro fuzzy based healthcare system using IoT,” in 2017 international conference on energy, communication, data analytics and soft computing (ICECDS), 2017, pp. 2299–2303.
D. V. Rahmawati, M. N. R. Hadjam, and T. Afiatin, “Relationship Between Behavioral Tendencies to Access Porn Sites and Religiosity in Adolescents,” J. Psikol., vol. 29, no. 1, pp. 1–13, 2002.
J. A. M. Basilio, G. A. Torres, G. S. Perez, L. K. T. Medina, and H. M. P. Meana, “Explicit image detection using YCbCr space color model as skin detection,” Appl. Math. Comput. Eng., pp. 123–128, 2011.
M. A. Rahman, I. K. E. Purnama, and M. H. Purnomo, “Simple method of human skin detection using HSV and YCbCr color spaces,” in 2014 International Conference on Intelligent Autonomous Agents, Networks and Systems, 2014, pp. 58–61.
H. Al Fatta, S. Pariyasto, and W. W. Widiyanto, “Prototype of Pornographic Image Detection with YCbCr and Color Space (RGB) Methods of Computer Vision,” in 2019 International Conference on Information and Communications Technology (ICOIACT), 2019, pp. 117–122.
D. Hardiyanto and D. A. Sartika, “Identification of Negative Content in Digital Image Based on Body Vital Signs Using Feature Extraction of GLCM and YCbCr Color,” Setrum Sist. Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 6, no. 1, pp. 120–131, 2017.
E. R. Ariyanto, “Classification of Porn Images Using the C 4.5 Algorithm Based on The YCbCr Color Model and Shape Detector,” Techno. Com, vol. 15, no. 2, pp. 92–98, 2016.
T. Afirianto and F. Amalia, “HSCbCrAB Color Model for Skin Detection Using PCA-kNN,” An Int. J. Inf. Commun. Technol., vol. 2, no. 2, 2017.
J. A. Marcial-Basilio, G. Aguilar-Torres, G. Sánchez-Pérez, L. K. Toscano-Medina, and H. M. Perez-Meana, “Detection of pornographic digital images,” Int. J. Comput., vol. 5, no. 2, pp. 298–305, 2011.
M. B. Garcia, T. F. Revano, B. G. M. Habal, J. O. Contreras, and J. B. R. Enriquez, “A pornographic image and video filtering application using optimized nudity recognition and detection algorithm,” in 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018, pp. 1–5.
J. A. Marcial Basilio, G. Aguilar Torres, G. Sánchez Pérez, K. Toscano Medina, and H. M. Pérez Meana, “Novel method for pornographic image detection using HSV and YCbCr color models,” Rev. Fac. Ing. Univ. Antioquia, no. 64, pp. 79–90, 2012.
R. Arora and N. Arora, “Analysis of SDLC models,” Int. J. Curr. Eng. Technol., vol. 6, no. 1, pp. 268–272, 2016.
B. Fatkhurrozi, M. A. Muslim, and D. R. Santoso, “The Use of Artificial Neuro Fuzzy Inference System (ANFIS) in Determining the Activity Status of Mount Merapi,” J. EECCIS (Electrics, Electron. Commun. Control. Informatics, Syst., vol. 6, no. 2, pp. 113–118, 2012.
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2023 Beki Subaeki, YA Gerhana, M B K Rusyana, Khaerul Manaf
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
-
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License
