RT Journal Article
T1 Learning strategies for sensitive content detection
A1 Povedano Álvarez, Daniel
A1 Sandoval Orozco, Ana Lucila
A1 Portela García-Miguel, Javier
A1 García Villalba, Luis Javier
A2 Guo, Zhenhua
AB Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.
PB MDPI
YR 2023
FD 2023-06-01
LK https://hdl.handle.net/20.500.14352/91319
UL https://hdl.handle.net/20.500.14352/91319
LA eng
NO Povedano Álvarez, D.; Sandoval Orozco, A.L.; García-Miguel, J.P.; García Villalba, L.J. Learning Strategies for Sensitive Content Detection. Electronics 2023, 12, 2496.
NO HEROES project (http://heroes-fct.eu, accessed on 4 February 2023)
NO European Union’s Horizon 2020
DS Docta Complutense
RD 23 ago 2024