An attention mechanism-based CNN-BiLSTM classification model for detection of inappropriate content in cartoon videos.

This paper proposes a novel method that combines an ImageNet pretrained convolutional neural network (CNN) with attention-based bidirectional long short-term memory (BiLSTM) network for accurate detection of inappropriate content in animated cartoon videos. The EfficientNet-B7 architecture is used as a pretrained CNN model for extracting features from videos, whilst the attention-based BiLSTM is implemented to dynamically focus on different parts of video feature sequences that are most relevant for classification. The whole architecture is trained end-to-end with input being the video frames and performed multiclass classification by classifying videos into three different categories namely safe, violent, and sexually explicit videos. This model is validated on a cartoon video dataset retrieved from YouTube by performing a search through YouTube Data API. The experimental results demonstrated that our model performs relatively better than other models by achieving an accuracy of 95.30%. Furthermore, the performance comparison with state-of-the-art algorithms showed that the proposed attention mechanism-based CNN-BiLSTM model achieved competitive results. [ABSTRACT FROM AUTHOR]