Skip to main content
SpringerLink
Log in

Objectionable Image Content Classification Using CNN-Based Semi-supervised Learning

  • Conference paper
  • First Online:
Advances in Smart Vehicular Technology, Transportation, Communication and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 347))

  • 249 Accesses

Abstract

Due to the increased online activity, people of all ages, especially adolescents, may get exposed to objectionable image content such as internet pornography. These images are spread quickly and widely over the internet, which causes serious social problems. Many researchers have proposed objectionable image content classification models by utilizing deep neural networks to prevent such images from being retrieved while surfing the web. The performance of such models can be enhanced by the semi-supervised learning method by effectively utilizing the image data from an overwhelming number of unlabeled objectionable samples. For many such unlabeled objectionable images, this paper proposes a semi-supervised image content classification framework using a balanced sample inclusion mechanism based on a higher class probability outcome to include the pseudo labels effectively in the existing model. The proposed framework fully utilizes semi-supervised learning and gradually improves model classification accuracy and reliability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/alex000kim/nsfw_data_scraper.

References

  1. Jevremovic, A., Veinovic, M., Cabarkapa, M., Krstic, M., Chorbev, I., Dimitrovski, I., Garcia, N., Pombo, N., Stojmenovic, M.: Keeping children safe online with limited resources: analyzing what is seen and heard. IEEE Access 9, 132, 723–132, 732 (2021). https://doi.org/10.1109/ACCESS.2021.3114389

  2. Chen, J., Liang, G., He, W., Xu, C., Yang, J., Liu, R.: A pornographic images recognition model based on deep one-class classification with visual attention mechanism. IEEE Access 8, 122, 709–122, 721 (2020). https://doi.org/10.1109/ACCESS.2020.2988736

  3. Brown, J.D., L’Engle, K.L.: X-Rated: Sexual attitudes and behaviors associated with U.S. early adolescents’ exposure to sexually explicit media. Commun. Res. 36(1), 129–151 (2009). https://doi.org/10.1177/0093650208326465

  4. Ybarra, M.L., Strasburger, V.C., Mitchell, K.J.: Sexual media exposure, sexual behavior, and sexual violence victimization in adolescence. Clin. Pediatr. 53(13), 1239–1247 (2014). https://doi.org/10.1177/0009922814538700

    Article  Google Scholar 

  5. Cohen-Almagor, R.: Online child sex offenders: challenges and counter-measures. Howard J. Crim. Justice 52(2), 190–215 (2013). https://doi.org/10.1111/hojo.12006

    Article  Google Scholar 

  6. Ybarra, M.L., Mitchell, K.J., Hamburger, M., Diener-West, M., Leaf, P.J.: X-rated material and perpetration of sexually aggressive behavior among children and adolescents: is there a link? Aggressive Behavior 37(1) (2011). 10.1002/ab.20367

    Google Scholar 

  7. Lee, J.S., Kuo, Y.M., Chung, P.C., Chen, E.L.: Naked image detection based on adaptive and extensible skin color model. Pattern Recognit. 40(8), 2261–2270 (2007). https://doi.org/10.1016/j.patcog.2006.11.016

    Article  MATH  Google Scholar 

  8. Zhu, H., Zhou, S., Wang, J., Yin, Z.: An algorithm of pornographic image detection. In: Fourth International Conference on Image and Graphics (ICIG 2007), pp. 801–804 (2007). https://doi.org/10.1109/ICIG.2007.29

  9. Yan, C.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). https://doi.org/10.1016/j.jvcir.2014.03.005

    Article  Google Scholar 

  10. Srisaan, C.: A classification of internet pornographic images. Int. J. Electron. Commer. Stud. 7(1), 95–104 (2016). https://doi.org/10.7903/ijecs.1408

    Article  Google Scholar 

  11. Zhuo, L., Geng, Z., Zhang, J., Li, X.G.: ORB feature based web pornographic image recognition. Neurocomputing 173, 511–517 (2016). https://doi.org/10.1016/j.neucom.2015.06.055

  12. Zhang, F., Wu, T.Y., Zheng, G.: Video salient region detection model based on wavelet transform and feature comparison. EURASIP J. Image Vid. Process. 2019(1), 58 (2019). https://doi.org/10.1186/s13640-019-0455-2

    Article  Google Scholar 

  13. Zhang, Y.J., Chen, J.Y., Lu, Z.M.: Face anti-spoofing detection based on color texture structure analysis. J. Netw. Intell. 7(2), 319–331 (2022)

    Google Scholar 

  14. Huu, P.N., Tien, D.N., Manh, K.N.: Action recognition application using artificial intelligence for smart social surveillance system. J. Inf. Hiding Multimed. Signal Process. 13(1), 1–11 (2022)

    Google Scholar 

  15. Kumar, S., Damaraju, A., Kumar, A., Kumari, S., Chen, C.M.: LSTM network for transportation mode detection. J. Internet Technol. 22(4), 891–902 (2021)

    Article  Google Scholar 

  16. Gao, J., Zou, H., Zhang, F., Wu, T.Y.: An intelligent stage light-based actor identification and positioning system. Int. J. Inf. Comput. Secur. 18(1–2), 204–218 (2022). https://doi.org/10.1504/IJICS.2022.122920

    Article  Google Scholar 

  17. Zhang, F., Wu, T.Y., Pan, J.S., Ding, G., Li, Z.: Human motion recognition based on SVM in VR art media interaction environment. Hum.-Centric Comput. Inf. Sci. 9(1), 40 (2019). https://doi.org/10.1186/s13673-019-0203-8

    Article  Google Scholar 

  18. Wang, R.B., An, Z.W., Wang, W.F., Yin, S., Xu, L.: A multi-stage data augmentation approach for imbalanced samples in image recognition. J. Netw. Intell. 6(1), 94–106 (2021)

    Google Scholar 

  19. Tawfeeq, L.A., Hussein, S.S.: Predication of most significant features in medical image by utilized CNN and heatmap. J. Inf. Hiding Multimed. Signal Process. 12(4), 217–225 (2021)

    Google Scholar 

  20. Yan, H.: Detect and depornize pornographic images using pre-trained CNN models. In: 2020 International Conference on Computing and Data Science (CDS), pp. 48–51 (2020). https://doi.org/10.1109/CDS49703.2020.00017

  21. Cheng, F., Wang, S.L., Wang, X.Z., Liew, A.W.C., Liu, G.S.: A global and local context integration DCNN for adult image classification. Pattern Recognit. 96, 106, 983 (2019). https://doi.org/10.1016/j.patcog.2019.106983

  22. 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

    Article  Google Scholar 

  23. Shen, R., Zou, F., Song, J., Yan, K., Zhou, K.: EFUI: an ensemble framework using uncertain inference for pornographic image recognition. Neurocomputing 322, 166–176 (2018). https://doi.org/10.1016/j.neucom.2018.08.080

    Article  Google Scholar 

  24. Lin, X., Qin, F., Peng, Y., Shao, Y.: Fine-grained pornographic image recognition with multiple feature fusion transfer learning. Int. J. Mach. Learn. Cybern. 12(1), 73–86 (2021). https://doi.org/10.1007/s13042-020-01157-9

    Article  Google Scholar 

  25. Dong, Z., Chen, Z.: Semi-supervised cell classification based on deep learning. In: The 6th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2022, pp. 49–52. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3522749.3523086

  26. Wu, M.E., Syu, J.H., Chen, C.M.: Kelly-based options trading strategies on settlement date via supervised learning algorithms. Comput. Econ. 59(4), 1627–1644 (2022). https://doi.org/10.1007/s10614-021-10226-2

    Article  Google Scholar 

  27. Zeng, X., Martinez, T.R.: Distribution-balanced stratified cross-validation for accuracy estimation. J. Exp. Theor. Artif. Intell. 12(1), 1–12 (2000). https://doi.org/10.1080/095281300146272

    Article  MATH  Google Scholar 

  28. Tseng, K.K., Zhang, R., Chen, C.M., Hassan, M.M.: DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service. J. Supercomput. 77(4), 3594–3615 (2021). https://doi.org/10.1007/s11227-020-03407-7

    Article  Google Scholar 

  29. Yu, G., Sun, K., Xu, C., Shi, X.H., Wu, C., Xie, T., Meng, R.Q., Meng, X.H., Wang, K.S., Xiao, H.M., Deng, H.W.: Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nat. Commun. 12(1), 6311 (2021). https://doi.org/10.1038/s41467-021-26643-8

    Article  Google Scholar 

  30. Rubin, M., Stein, O., Turko, N.A., Nygate, Y., Roitshtain, D., Karako, L., Barnea, I., Giryes, R., Shaked, N.T.: TOP-GAN: stain-free cancer cell classification using deep learning with a small training set. Med. Image Anal. 57, 176–185 (2019). https://doi.org/10.1016/j.media.2019.06.014

    Article  Google Scholar 

  31. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017). https://doi.org/10.1109/CVPR.2017.243

  32. Bisong, E.: Google Colaboratory. Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 59–64 (2019). https://doi.org/10.1007/978-1-4842-4470-8_7

Download references

Acknowledgements

The work was supported by the Council of Scientific and Industrial Research (CSIR), Ministry of Science and Technology, Govt. of India (Sanction No.: 22(0836)/20/EMR-II).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM) Dhanbad, Jharkhand, 826004, India

    Shukla Mondal & Arup Kumar Pal

  2. Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani, Kanchrapara, West Bengal, 741235, India

    Shukla Mondal & SK Hafizul Islam

  3. Department of Computing and Information Technologies, Rochester Institute of Technology, RIT Global Campus, Dr. Shpetim Robaj, n.n., Germia Campus, Prishtina, 10000, Kosovo

    Debabrata Samanta

Authors
  1. Shukla Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arup Kumar Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. SK Hafizul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Debabrata Samanta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to SK Hafizul Islam .

Editor information

Editors and Affiliations

  1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China

    Shaoquan Ni

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Tsu-Yang Wu

  3. China Railway Economic and Planning Research Institute, Beijing, China

    Jingchun Geng

  4. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu

  5. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mondal, S., Pal, A.K., Islam, S.H., Samanta, D. (2023). Objectionable Image Content Classification Using CNN-Based Semi-supervised Learning. In: Ni, S., Wu, TY., Geng, J., Chu, SC., Tsihrintzis, G.A. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 347. Springer, Singapore. https://doi.org/10.1007/978-981-99-0848-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0848-6_23

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0847-9

  • Online ISBN: 978-981-99-0848-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation