Abstract
The rapid growth of the Internet and online social network activities makes possible to fast share a huge volume of various digital multimedia contents such as images and videos. However, the easy accessibility of pornographic contents by general users, particularly teenagers, is problematic as well as the rare availability of the legitimate benchmark datasets for the pornographic image detection makes the research rather challenging. In this paper, we present a transfer learning approach, for which the existing general deep learning network is adopted for pornographic image detection problem. We consider five well-known deep learning models, which include VGG16, MobilNet, InceptionV3, Xception, and ResNet50, and each general deep learning network architecture is fine-turned to detect pornographic images. The experimental result with NPDI pornographic database demonstrates the effectiveness of the proposed transfer learning approach with promising detection accuracy.
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References
Zhu, R., Wu, X., Zhu, B., Song, L.: Application of pornographic images recognition based on depth learning. In: Proceedings of the 2018 International Conference on Information Science and System - ICISS 2018 (2018). https://doi.org/10.1145/3209914.3209946
Sengamedu, S., Sanyal, S., Satish, S.: Detection of pornographic content in internet images. In: Proceedings of the 19th ACM International Conference on Multimedia - MM 2011 (2011). https://doi.org/10.1145/2072298.2071959
Zuo, H., Hu, W., Wu, O.: Patch-based skin color detection and its application to pornography image filtering. In: Proceedings of the 19th International Conference on World Wide Web - WWW 2010 (2010). https://doi.org/10.1145/1772690.1772887
Platzer, C., Stuetz, M., Lindorfer, M.: Skin sheriff. In: Proceedings of the 2nd International Workshop on Security and Forensics in Communication Systems - SFCS 2014 (2014). https://doi.org/10.1145/2598918.2598920
Karamizadeh, S., Arabsorkhi, A.: Methods of pornography detection. In: Proceedings of the 10th International Conference on Computer Modeling and Simulation - ICCMS 2018 (2018). https://doi.org/10.1145/3177457.3177484
Jansohn, C., Ulges, A., Breuel, T.: Detecting pornographic video content by combining image features with motion information. In: Proceedings of the seventeen ACM international conference on Multimedia - MM 2009 (2009). https://doi.org/10.1145/1631272.1631366
Avila, S., Thome, N., Cord, M., Valle, E., Araújo, A.D.: Pooling in image representation: the visual codeword point of view. Computer Vision and Image Understanding 117(5), 453–465 (2013). https://doi.org/10.1016/j.cviu.2012.09.007
Perez, M., et al.: Video pornography detection through deep learning techniques and motion information. Neurocomputing 230, 279–293 (2017). https://doi.org/10.1016/j.neucom.2016.12.017
Nian, F., Li, T., Wang, Y., Xu, M., Wu, J.: Pornographic image detection utilizing deep convolutional neural networks. Neurocomputing 210, 283–293 (2016). https://doi.org/10.1016/j.neucom.2015.09.135
Zhuo, L., Geng, Z., Zhang, J., Li, X.: ORB feature based web pornographic image recognition. Neurocomputing 173, 511–517 (2016). https://doi.org/10.1016/j.neucom.2015.06.055
Zhang, J., Sui, L., Zhuo, L., Li, Z., Yang, Y.: An approach of bag-of-words based on visual attention model for pornographic images recognition in compressed domain. Neurocomputing 110, 145–152 (2013)
Yan, C., Liu, Y., Xie, H., Liao, Z., Yin, J.: Extracting salient region for pornographic image detection. J. Vis. Commun. Image Represent. 25(5), 1130–1135 (2014)
Home - Keras Documentation Keras.io (2019). https://keras.io
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, CoRR 14091556 (2014)
Howard, A., et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv e-prints, arXiv:1704.04861 (2017)
Szegedy, C., et al.: Going Deeper with Convolutions, arXiv e-prints, arXiv:1409.4842 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision, arXiv e-prints, arXiv:1512.00567 (2015). Accessed 18 Apr 2019
Francois, C.: Xception: Deep Learning with Depthwise Separable Convolutions, arXiv e-prints, arXiv:1610.02357 (2016)
Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian, S.: Deep Residual Learning for Image Recognition, arXiv e-prints, arXiv:1512.03385 (2015)
Jin, X., Wang, Y., Tan, X.: Pornographic Image Recognition via Weighted Multiple Instance Learning arXiv e-prints, arXiv:1902.03771v1 (2019)
Jung, J., Makhijani, R., Morlot, A.: Combining CNNs for detecting pornography in the absence of labeled training data (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/tpami.2016.2577031
Girshick, R.: Fast R-CNN. In: ICCV, pp. 1440–1448 (2015)
Huang, Y., Kong, A.W.K.: Using a CNN ensemble for detecting pornographic and upskirt images. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) (2016)
Lienhart, R., Hauke, R.: Filtering adult image content with topic models. In: IEEE International Conference on Multimedia and Expo. ICME 2009, pp. 1472–1475 (2009)
Acknowledgements
We would like to convey our gratitude to Professor Sandra Avila and her team for letting us use the NPDI dataset in a short notice.
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Ashan, B., Cho, H., Liu, Q. (2020). Performance Evaluation of Transfer Learning for Pornographic Detection. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_43
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DOI: https://doi.org/10.1007/978-3-030-32591-6_43
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