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A survey of artificial intelligence strategies for automatic detection of sexually explicit videos

  • 1167: Data Science on Multimedia Data: Challenges and Applications
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Abstract

Digital forensics and analysis have emerged as a discipline to fight against cyber and computer-assisted crime. In particular, taking into account the increasing of unconstrained pornographic content over Internet and the spreading cases of Child Sex Abuse material distribution, there is a growing need of efficient computational tools to automatically detect or/and block pornographic videos. The primary objective of this study is to review the different strategies available in the literature for pornography detection in videos and identify research gaps. This survey shows that deep learning based techniques detect videos with sexually explicit content more accurately compared with other conventional detection strategies. The accuracy of the strategies reported in this work, is found to be dependent on features extraction techniques, architecture, and learning algorithms. Finally, further research areas in pornographic video detection are outlined.

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Acknowledgements

This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No 700326. Website: http://ramses2020.eu

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Authors and Affiliations

  1. Santander Big Data Institute, Universidad Carlos III de Madrid, Getafe, España

    Jenny Cifuentes

  2. Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040, Madrid, España

    Ana Lucila Sandoval Orozco & Luis Javier García Villalba

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  1. Jenny Cifuentes
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  2. Ana Lucila Sandoval Orozco
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  3. Luis Javier García Villalba
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Correspondence to Jenny Cifuentes.

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Cifuentes, J., Sandoval Orozco, A.L. & García Villalba, L.J. A survey of artificial intelligence strategies for automatic detection of sexually explicit videos. Multimed Tools Appl 81, 3205–3222 (2022). https://doi.org/10.1007/s11042-021-10628-2

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