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A Novel Approach for Image based Cyberbullying Detection and Prevention

by Vijayakumar V., Hari Prasad D., Adolf P.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 22
Year of Publication: 2021
Authors: Vijayakumar V., Hari Prasad D., Adolf P.
10.5120/ijca2021921591

Vijayakumar V., Hari Prasad D., Adolf P. . A Novel Approach for Image based Cyberbullying Detection and Prevention. International Journal of Computer Applications. 183, 22 ( Aug 2021), 41-45. DOI=10.5120/ijca2021921591

@article{ 10.5120/ijca2021921591,
author = { Vijayakumar V., Hari Prasad D., Adolf P. },
title = { A Novel Approach for Image based Cyberbullying Detection and Prevention },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2021 },
volume = { 183 },
number = { 22 },
month = { Aug },
year = { 2021 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number22/32060-2021921591/ },
doi = { 10.5120/ijca2021921591 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:34.833563+05:30
%A Vijayakumar V.
%A Hari Prasad D.
%A Adolf P.
%T A Novel Approach for Image based Cyberbullying Detection and Prevention
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 22
%P 41-45
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day’s social media offer great communication opportunities and also it increases the addiction and raises vulnerability of young people to threatening situations online. The cyberbullying is one of the most affected cybercrime that has gained more attention in recent years. It is an aggressive, intentional behaviour that is carried out by a group or individual, repeatedly and over time against a victim. Most of the current works have focused on detecting cyberbullying based on textual information and very few are based on image cyberbullying. Due to inexpensive of the internet cost, image-based bullying is growing. To detect and prevent, many works have been done by researchers with novel technologies. Chatbot is a really valuable tool to help prevent cyberbullying which simplify the interaction between humans and computers. This paper proposed a Convolutional Neural Network (CNN) deep model to predict the cyber bullying. If a cyber bullying image is detected, it gives the suitable alert messages to the users, parents and caretaker through chatbot interface. The experiments are conducted on publicly available social media datasets and tested with telegram chat communication. This paper aims to direct future research on integrating multimodal data sources such as text and images to prevent cyberbullying issues.

References
  1. S. Salawu, Y. He, and J. Lumsden, “Approaches to Automated Detection of Cyberbullying: A Survey,” IEEE Trans. Affect. Comput., vol. 11, no. 1, pp. 3–24, 2020, doi: 10.1109/TAFFC.2017.2761757.
  2. A. Singh and M. Kaur, “Content-based cybercrime detection: A concise review,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 8, pp. 1193–1207, 2019.
  3. H. Rosa et al., “Automatic cyberbullying detection: A systematic review,” Comput. Human Behav., vol. 93, no. October 2018, pp. 333–345, 2019, doi: 10.1016/j.chb.2018.12.021.
  4. N. Tahmasbi and A. Fuchsberger, “ChatterShield – A multi-platform cyberbullying detection system for parents,” 25th Am. Conf. Inf. Syst. AMCIS 2019, pp. 1–5, 2019.
  5. Y. S. M. V. Tanmayee Patange, Jigyasa Singh, Aishwarya Thorve, “DETECTION OF CYBERHECTORING ON INSTAGRAM,” pp. 5–8, 2019.
  6. S. V. Georgakopoulos, A. G. Vrahatis, S. K. Tasoulis, and V. P. Plagianakos, “Convolutional neural networks for toxic comment classification,” ACM Int. Conf. Proceeding Ser., no. April, 2018, doi: 10.1145/3200947.3208069.
  7. T. Pradheep, J. . Sheeba, T. Yogeshwaran, and S. Pradeep Devaneyan, “Automatic Multi Model Cyber Bullying Detection from Social Networks,” SSRN Electron. J., pp. 248–254, 2018, doi: 10.2139/ssrn.3123710.
  8. M. A. Al-Ajlan and M. Ykhlef, “Deep learning algorithm for cyberbullying detection,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 9, pp. 199–205, 2018, doi: 10.14569/ijacsa.2018.090927.
  9. M. Dadvar and K. Eckert, “Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study,” pp. 1–13, 2018, [Online]. Available: http://arxiv.org/abs/1812.08046.
  10. A. Gangwar, E. Fidalgo, E. Alegre, and V. González-Castro, “Pornography and child sexual abuse detection in image and video: A comparative evaluation,” IET Semin. Dig., vol. 2017, no. 5, pp. 37–42, 2017, doi: 10.1049/ic.2017.0046.
  11. Q. H. Nguyen, K. N. K. Nguyen, H. L. Tran, T. T. Nguyen, D. D. Phan, and D. L. Vu, “Multi-level detector for pornographic content using CNN models,” Proc. - 2020 RIVF Int. Conf. Comput. Commun. Technol. RIVF 2020, 2020, doi: 10.1109/RIVF48685.2020.9140734.
  12. Nishant Vishwamitra, Hongxin Hu, Feng Luo and Long Cheng, “Towards Understanding and Detecting Cyberbullying in Real-world Images ,” In Proceedings of the 28th Network and Distributed System Security Symposium (NDSS 2021), February 21-24, 2021.
  13. K. Wang, Q. Xiong, C. Wu, M. Gao and Y. Yu, "Multi-modal cyberbullying detection on social networks," 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9206663.
  14. https://www.helpguide.org/articles/abuse/bullying-and-cyberbullying.htm#
  15. https://www.stopbullying.gov/cyberbullying/what-is-it
  16. https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/
  17. Islam, Manowarul & Uddin, Md Ashraf & Islam, Linta & Akhter, Arnisha & Sharmin, Selina & Acharjee, Uzzal. (2021). Cyberbullying Detection on Social Networks Using Machine Learning Approaches. 10.1109/CSDE50874.2020.9411601.
  18. R.Cohen, N.Mathiarasu, R.Aarif, S.Ansari, D.Fraser, M.Hegde, J.Henderson, I.Kajic, A.Khan, Z.Liao, A.Mancisidor, S.Nagpal, A.Pham, A.Saini, J.Shen, H.Singh, C.Tavares and S.Thandra, “An education-based approach to aid in the prevention of cyberbullying,” ACM Computers & Society, Volume 47, Issue 4, July 2018, 17–28.
  19. M. W. Al-Nabki, E. Fidalgo, R. A. Vasco-Carofilis, F. Jañez-Martino, and J. Velasco-Mata, “Evaluating Performance of an Adult Pornography Classifier for Child Sexual Abuse Detection,” 2020, [Online]. Available: http://arxiv.org/abs/2005.08766.
  20. A. Tabone, A. Bonnici, S. Cristina, R. Farrugia, and K. Camilleri, “Private body part detection using deep learning,” ICPRAM 2020 - Proc. 9th Int. Conf. Pattern Recognit. Appl. Methods, pp. 205–211, 2020, doi: 10.5220/0009101502050211.
  21. S. M. Kia, H. Rahmani, R. Mortezaei, M. E. Moghaddam, and A. Namazi, “A Novel Scheme for Intelligent Recognition of Pornographic Images,” 2014, [Online]. Available: http://arxiv.org/abs/1402.5792.
  22. K. Zhou, L. Zhuo, Z. Geng, J. Zhang, and X. G. Li, “Convolutional neural networks based pornographic image classification,” Proc. - 2016 IEEE 2nd Int. Conf. Multimed. Big Data, BigMM 2016, pp. 206–209, 2016, doi: 10.1109/BigMM.2016.29.
  23. https://github.com/EBazarov
  24. Vijayakumar V and Hari Prasad D, “Intelligent Chatbot Development for Text based Cyberbullying Prevention,” International Journal of New Innovations in Engineering and Technology, Volume 17, Issue 1, pp. 73-81, June 2021.
Index Terms

Computer Science
Information Sciences

Keywords

Cyber bullying Image detection Image based deep learning Models Cyberbullying prevention

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