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Obscenity detection transformer for detecting inappropriate contents from videos

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Abstract

With the availability of a wide range of images and videos on the Internet, classification and detection of inappropriate content has become a matter of serious concern. This type of content has a harmful impact on the minds of minors as well as on adults. Therefore, it is necessary to control and detect such content from images and videos. Recent research has focused on deep-learning-based automated pornographic detection, a bold move to replace humans in the time-consuming task of moderating online content. This paper is based on the idea that incorporating detailed information into a model helps solve the problem of mapping pornographic content. In this paper, a novel deep-learning transformer-based framework namely, Obscenity Detection Transformer (ODT) is proposed to detect and classify inappropriate or pornographic content from videos. The proposed transformer inputs video frames and leverages the vision transformer with the LSTM layer. LSTM embedding enables the network to extract more informative features. Also, GELU activation-based MLP is employed to classify pornographic and non-pornographic content. The advantage of leveraging transformer-based architecture is that these architectures improve efficiency and accuracy when compared with CNN-based models. To validate the efficiency and efficacy of the proposed model, extensive experiments are carried out on Pornography-2 k and Pornography-800 datasets. The proposed model outperforms the current state-of-the-art (CNN) in terms of computational efficiency and accuracy. The accuracies achieved for the two aforementioned datasets are 99.6% and 98.8%, respectively.

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Authors and Affiliations

  1. Delhi Technological University, Delhi, India

    Kamakshi Rautela, Dhruv Sharma & Dinesh Kumar

  2. Dr B R Ambedkar National Institute of Technology, Jalandhar, India

    Vijay Kumar

Authors
  1. Kamakshi Rautela
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  2. Dhruv Sharma
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  3. Vijay Kumar
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  4. Dinesh Kumar
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Correspondence to Vijay Kumar.

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Rautela, K., Sharma, D., Kumar, V. et al. Obscenity detection transformer for detecting inappropriate contents from videos. Multimed Tools Appl 83, 10799–10814 (2024). https://doi.org/10.1007/s11042-023-16078-2

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