Convolutional Neural Network-based Transfer Learning and Classification of Visual Contents for Film Censorship

Main Article Content

Hor Sui Lyn
Sarina Mansor
Nouar AlDahoul
Hezerul Abdul Karim

Abstract

Content filtering is gaining popularity due to easy exposure of explicit visual contents to the public. Excessive exposure of inappropriate visual contents can cause devastating effects such as the growth of improper mindset and rise of societal issues such as free sex, child abandonment and rape cases. At present, most of the broadcasting media sites are hiring censorship editors to label graphic contents manually. Nevertheless, the efficiency is limited by factors such as the attention span of humans and the training required for the editors. This paper proposes to study the effect of usage of Convolutional Neural Network (CNN) as feature extractor coupled with Support Vector Machine (SVM) as classifier in an automated pornographic detection system. Three CNN architectures: MobileNet, Visual Geometry Group-19 (VGG-19) and Residual Network-50 Version 2 (ResNet50_V2), and two classifiers: CNN and SVM were utilized to explore the combination that produce the best result. Frames of films fed as input into the CNN were classified into two groups: porn or non-porn. The best accuracy was 92.80 % obtained using fine-tuned ResNet50_V2 as feature extractor and SVM as classifier. Transfer learning and SVM have improved the CNN model by approximately 10 %.

Article Details

Section
Articles

References

M. Short, L. Black, A. Smith, C. Wetterneck and D. Wells, "A Review of Internet Pornography Use Research: Methodology and Content from the Past 10 Years," Cyberpsychology, Behavior, and Social Networking, vol. 15, no. 1, pp. 13-23, 2012.

E. Owens, R. Behun, J. Manning and R. Reid, "The Impact of Internet Pornography on Adolescents: A Review of the Research," Sexual Addiction & Compulsivity, vol. 19, no. 1-2, pp. 99-122, 2012.

L. Torrey and J. Shavlik, "Transfer Learning," in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, E. Soria, J. Martin, R. Magdalena, M. Martinez and A. Serrano, Ed. IGI Global, pp. 242-264, 2009.

M. Moustafa, "Applying Deep Learning to Classify Pornographic Images and Videos, in 7th Pacific-Rim Symposium on Image and Video Technology (PSIVT 2015), Auckland, 2015.

A. Krizhevsky, I. Sutskever and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going Deeper with Convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.

F. Nian, T. Li, Y. Wang, M. Xu and J. Wu, "Pornographic Image Detection Utilizing Deep Convolutional Neural Networks," Neurocomputing, vol. 210, pp. 283-293, 2016.

X. Ou, H. Ling, H. Yu, P. Li, F. Zou and S. Liu, "Adult Image and Video Recognition by A Deep Multicontext Network and Fine-To-Coarse Strategy," ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 5, pp. 1-25, 2017.

Z. Ying, P. Shi, D. Pan, H. Yang and M. Hou, "A Deep Network for Pornographic Image Recognition Based on Feature Visualization Analysis," in 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, pp. 212-216, 2018.

I. Agastya, A. Setyanto, Kusrini and D. Handayani, "Convolutional Neural Network for Pornographic Images Classification," in 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), Subang Jaya, 2018.

J. Wehrmann, G. Simões, R. Barros and V. Cavalcante, "Adult Content Detection in Videos with Convolutional and Recurrent Neural Networks," Neurocomputing, vol. 272, pp. 432-438, 2018.

N. AlDahoul, H. A. Karim, M. H. Lye Abdullah, M. F. Ahmad Fauzi, S. Mansour and J. See, "Local Receptive Field-Extreme Learning Machine-based Adult Content Detection," in 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, pp. 128-133, 2019.

Z. Zhao and A. Cai, "Combining Multiple SVM Classifiers for Adult Image Recognition," in 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content, Beijing, pp. 149-153, 2010.

A. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Adreetto and H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," preprint:1704.04861, 2017.

K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in International Conference on Learning Representations, 2015.

K. He, X. Zhang, S. Ren and J. Sun, "Identity Mappings in Deep Residual Networks," in European Conference on Computer Vision, pp. 630-645, 2016.

"A Comprehensive Guide to Convolutional Neural Networks - the ELI5 Way," 2020. [Online]. Available: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53. [Accessed: 18- Nov- 2020].

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Prentice Hall, 2010.

F. Chollet, Deep Learning with Python, 1st ed. Manning Publications, pp. 104-110, 2017.

C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.

R. Ruiz-Gonzalez, J. Gomez-Gil, F. J Gomez-Gil and V. Martínez-Martínez, "An SVM-Based Classifier for Estimating The State of Various Rotating Components in Agro-Industrial Machinery with A Vibration Signal Acquired from A Single Point on The Machine Chassis," Sensors, vol. 14, pp. 20713-20735, 2014.

J. Davis and M. Goadrich, "The Relationship Between Precision-Recall and ROC Curves," in Proceedings of The 23rd International Conference on Machine Learning, pp. 233-240, 2006.

"Scikit-learn: Machine Learning in Python - scikit-learn 0.22.1 documentation", Scikit-learn.org, 2020. [Online]. Available: https://scikit-learn.org/stable/. [Accessed: 16- Jan- 2020].