An Evaluation of Traditional and CNN-Based Feature Descriptors for Cartoon Pornography Detection
| Autor: | Mohammad Faizal Ahmad Fauzi, Nouar AlDahoul, Sarina Mansor, Myles Joshua Toledo Tan, Abdulaziz Saleh Ba Wazir, Hezerul Abdul Karim, Hor Sui Lyn, Mohd Haris Lye Abdullah |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Computer science media_common.media_genre Feature extraction 02 engineering and technology Animated cartoon transfer learning Convolutional neural network 020901 industrial engineering & automation convolutional neural networks 0202 electrical engineering electronic engineering information engineering Pornography General Materials Science Cartoon animation domain generalization media_common business.industry General Engineering Pattern recognition Visualization Feature (computer vision) pornography detection 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering F1 score Transfer of learning business feature and decision fusion lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 39910-39925 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Inappropriate visual content on the internet has spread everywhere, and thus children are exposed unintentionally to sexually explicit visual content. Animated cartoon movies sometimes have sensitive content such as pornography and sex. Usually, video sharing platforms take children’s e-safety into consideration through manual censorship, which is both time-consuming and expensive. Therefore, automated cartoon censorship is highly recommended to be integrated into media platforms. In this paper, various methods and approaches were explored to detect inappropriate visual content in cartoon animation. First, state-of-the-art conventional feature techniques were utilised and evaluated. In addition, a simple end-to-end convolutional neural network (CNN) was used and was found to outperform conventional techniques in terms of accuracy (85.33%) and F1 score (83.46%). Additionally, to target the deeper version of CNNs, ResNet, and EfficientNet were demonstrated and compared. The CNN-based extracted features were mapped into two classes: normal and porn. To improve the model’s performance, we utilised feature and decision fusion approaches which were found to outperform state-of-the-art techniques in terms of accuracy (87.87%), F1 score (87.87%), and AUC (94.40%). To validate the domain generalisation performance of the proposed methods, CNNs, pre-trained on the cartoon dataset were evaluated on public NPDI-800 natural videos and found to provide an accuracy of 79.92%, and F1 score of 80.58%. Similarly, CNNs, pre-trained on the public NPDI-800 natural videos, were evaluated on cartoon dataset and found to give an accuracy of 82.666%, and F1 score of 81.588%. Finally, a novel cartoon pornography dataset, with various characters, skin colours, positions, viewpoints, and scales, was proposed. |
| Databáze: | OpenAIRE |
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