Skip to main content
SpringerLink
Log in

A deep learning framework for finding illicit images/videos of children

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Recent advances in deep learning have led to tremendous achievements in computer vision applications. Specifically for the tasks of automated human age estimation and nudity detection, modern machine learning can predict whether or not an image contains nudity or the presence of a minor with startling accuracy. Fusing together separate models can make possible to identify instances of child pornography without ever coming into contact with the illicit material during model training. In this paper, a novel framework for automatically identifying Sexually Exploitative Imagery of Children is introduced. It is a synthesis of models for modeling human apparent age and nudity detection. The performance of this approach is thoroughly evaluated on several widely used age estimation and nudity detection datasets. Additionally, preliminary tests were conducted with the help of a local law enforcement agency on a private dataset of SEIC taken from real-world cases with up to \(97\%\) accuracy of SEIC video classification.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://www.missingkids.org/footer/media/keyfacts.

  2. https://www.microsoft.com/en-us/PhotoDNA.

  3. Referring to pictures taken in uncontrolled conditions.

  4. https://www.flickr.com/.

  5. https://www.youtube.com.

References

  1. Mahadeokar, J., Pesavento, G.: Open sourcing a deep learning solution for detecting nsfw images (2016)

  2. Rondeau, J., Alvarez, M.: Deep modeling of human age guesses for apparent age estimation. In: International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil (2018)

  3. Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: S3fd: Single shot scale-invariant face detector. CoRR abs/1708.05237 (2017). http://arxiv.org/abs/1708.05237

  4. Perez, M., Avila, S., Moreira, D., Moraes, D., Testoni, V., Valle, E., Goldenstein, S., Rocha, A.: Video pornography detection through deep learning techniques and motion information. Neurocomputing 230, 279–293 (2017)

    Article  Google Scholar 

  5. Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output cnn for age estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4920–4928 (2016)

  6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)

  7. Weyand, T., Kostrikov, I., Philbin, J.: Planet-photo geolocation with convolutional neural networks. In: European Conference on Computer Vision, pp. 37–55. Springer (2016)

  8. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711. Springer (2016)

  9. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  10. Fernández, C., Huerta, I., Prati, A.: A comparative evaluation of regression learning algorithms for facial age estimation. In: Face and Facial Expression Recognition from Real World Videos, pp. 133–144. Springer (2015)

  11. Guo, G., Mu, G., Fu, Y., Huang, T.S.: Human age estimation using bio-inspired features. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 112–119. IEEE (2009)

  12. Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 621–628 (2004)

    Article  Google Scholar 

  13. Liu, X., Li, S., Kan, M., Zhang, J., Wu, S., Liu, W., Han, H., Shan, S., Chen, X.: Agenet: Deeply learned regressor and classifier for robust apparent age estimation. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 16–24 (2015)

  14. Escalera, S., Fabian, J., Pardo, P., Baro, X., Gonzalez, J., Escalante, H.J., Misevic, D., Steiner, U., Guyon, I.: Chalearn looking at people 2015: Apparent age and cultural event recognition datasets and results. In: The IEEE International Conference on Computer Vision (ICCV) Workshops (2015)

  15. Escalera, S., Torres Torres, M., Martinez, B., Baro, X., Jair Escalante, H., Guyon, I., Tzimiropoulos, G., Corneou, C., Oliu, M., Ali Bagheri, M., Valstar, M.: Chalearn looking at people and faces of the world: Face analysis workshop and challenge 2016. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2016)

  16. Agustsson, E., Timofte, R., Escalera, S., Baro, X., Guyon, I., Rothe., R.: Apparent and real age estimation in still images with deep residual regressors on appa-real database. In: 12th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2017. IEEE (2017)

  17. Geng, X., Yin, C., Zhou, Z.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2401–2412 (2013)

    Article  Google Scholar 

  18. Geng, X.: Label distribution learning. IEEE Trans. Knowledge Data Eng. 28(7), 1734–1748 (2016)

    Article  Google Scholar 

  19. Yang, X., Geng, X., Zhou, D.: Sparsity conditional energy label distribution learning for age estimation. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2259–2265 (2016)

  20. Berger, A., Pietra, V., Pietra, S.: A maximum entropy approach to natural language processing. Comput. Ling. 22(1), 39–71 (1996)

    Article  Google Scholar 

  21. Geng, X., Hou, P.: Pre-release prediction of crowd opinion on movies by label distribution learning. In: International Conference on Artificial Intelligence (IJCAI), pp. 3511–3517 (2015)

  22. Xing, C., Geng, X., Xue, H.: Logistic boosting regression for label distribution learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4489–4497 (2016)

  23. Shen, W., Zhao, K., Guo, Y., Yuille, A.: Label distribution learning forests. In: Advances in Neural Information Processing Systems (NIPS), pp. 834–843 (2017)

  24. Gao, B., Xing, C., Xie, C., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26(6), 2825–2838 (2017)

    Article  MathSciNet  Google Scholar 

  25. Roberts, S.T.: Content moderation. Encyclopedia of Big Data pp. 1–4 (2017)

  26. Chen, A.: The laborers who keep dick pics and beheadings out of your facebook feed (2014)

  27. Ries, C., Lienhart, R.: A survey on visual adult image recognition. Multimedia Tools Appl. 69(3), 661–688 (2014)

    Article  Google Scholar 

  28. Caetano, C., Avila, S., Guimarães, S., Araújo, A.d.A.: Representing local binary descriptors with bossanova for visual recognition. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 49–54. ACM (2014)

  29. Valle, E., de Avila, S., Luz Jr, A.d., de Souza, F., Coelho, M., Araújo, A.: Content-based filtering for video sharing social networks. arXiv preprint arXiv:1101.2427 (2011)

  30. Sae-Bae, N., Sun, X., Sencar, H.T., Memon, N.D.: Towards automatic detection of child pornography. In: ICIP, pp. 5332–5336 (2014)

  31. Karavarsamis, S., Ntarmos, N., Blekas, K., Pitas, I.: Detecting pornographic images by localizing skin rois. Int. J. Digital Crime Forensics (IJDCF) 5(1), 39–53 (2013)

    Article  Google Scholar 

  32. Tanner, K.: Modeling automated detection of children in images. Master’s thesis, University of Rhode Island (2011)

  33. Ulges, A., Stahl, A.: Automatic detection of child pornography using color visual words. In: Multimedia and Expo (ICME), 2011 IEEE International Conference on, pp. 1–6. IEEE (2011)

  34. Moustafa, M.: Applying deep learning to classify pornographic images and videos. arXiv preprint arXiv:1511.08899 (2015)

  35. Nian, F., Li, T., Wang, Y., Xu, M., Wu, J.: Pornographic image detection utilizing deep convolutional neural networks. Neurocomputing 210, 283–293 (2016)

    Article  Google Scholar 

  36. Wehrmann, J., Simões, G.S., Barros, R.C., Cavalcante, V.F.: Adult content detection in videos with convolutional and recurrent neural networks. Neurocomputing 272, 432–438 (2018)

    Article  Google Scholar 

  37. de Avila, S.E.F., Thome, N., Cord, M., Valle, E., de Albuquerque Araújo, A.: Pooling in image representation: the visual codeword point of view. Comput. Vision Image Understand. 117, 453–465 (2013)

    Article  Google Scholar 

  38. Schulze, C., Henter, D., Borth, D., Dengel, A.: Automatic detection of csa media by multi-modal feature fusion for law enforcement support. In: Proceedings of International Conference on Multimedia Retrieval, p. 353. ACM (2014)

  39. Vitorino, P., Avila, S., Perez, M., Rocha, A.: Leveraging deep neural networks to fight child pornography in the age of social media. J. Vis. Commun. Image Represent 50, 303–313 (2018)

    Article  Google Scholar 

  40. Kloess, J.A., Woodhams, J., Whittle, H., Grant, T., Hamilton-Giachritsis, C.E.: The challenges of identifying and classifying child sexual abuse material. Sexual Abuse, p. 1079063217724768 (2017)

  41. Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int J. Comput. Vis. (2016)

  42. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: Imagenet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255. IEEE Computer Society (2009)

  43. Agustsson, E., Timofte, R., Escalera, S., Baró, X., Guyon, I., Rothe, R.: Apparent and real age estimation in still images with deep residual regressors on appa-real database. In: IEEE International Conference on Automatic Face Gesture Recognition (FG), pp. 87–94 (2017)

  44. Alvarez, S.P.: RedLight an efficient illicit image detection application for law enforcement. University of Rhode Island (2012)

  45. Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: A face detection benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

  46. Rothe, R., Timofte, R., Gool, L.V.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Computer Vis. (IJCV) (2016)

  47. Clapés, A., Bilici, O., Temirova, D., Avots, E., Anbarjafari, G., Escalera, S.: From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2373–2382 (2018)

Download references

Acknowledgements

This project was supported by Award No. 2016-MU-CX-K015 awarded by the National Institute of Justice, U.S. Department of Justice. We would also like to thank Christopher Toole for collecting the challenging images of adults and children dataset.

Author information

Authors and Affiliations

  1. University of Rhode Island, South Kingstown, USA

    Jared Rondeau, Douglas Deslauriers, Thomas Howard III & Marco Alvarez

Authors
  1. Jared Rondeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Douglas Deslauriers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Howard III
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Alvarez.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supported by NIJ/DOJ Award Number: 2016-MU-CX-K015.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rondeau, J., Deslauriers, D., Howard III, T. et al. A deep learning framework for finding illicit images/videos of children. Machine Vision and Applications 33, 66 (2022). https://doi.org/10.1007/s00138-022-01318-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-022-01318-6

Keywords

Search

Navigation