Abstract
Detecting pornographic content on the web is an important challenge in protecting users from inappropriate content. The heterogeneity of the web, the diversity of used languages, and the existence of implicit pornography using language that cannot be detected by keywords, make this task difficult. There are very few published works on text-based web classification. In this paper, we propose a novel approach that addresses these challenges. We tackle web porn detection based on multiple pages for the same website, by incorporating an attention mechanism to treat pages according to their respective importance, and the first to use transformers for web porn detection. Our method outperforms various other approaches that do not incorporate attention. With our multilingual solution, we achieved the accuracy of 91.59% on a hand-labeled test set for the task of porn detection.
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Yamoun, L., Guessoum, Z., Girard, C. (2023). Transformers and Attention Mechanism for Website Classification and Porn Detection. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_13
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