Dr. MD. ASHFAKUL HASAN, ROSHINI SINGH, S SANJANA, SUBHADIP MUDI, SIMRAN SINGH

Abstract

The exponential growth of videos on YouTube has attracted billions of viewers among which the majority belongs to a young demographic. Malicious up loaders also find this platform as an opportunity to spread upsetting visual content, such as using animated cartoon videos to share inappropriate content with children. Therefore, an automatic real-time video content filtering mechanism is highly suggested to be integrated into social media platforms. In this study, a novel deep learning-based architecture is proposed for the detection and classification of inappropriate content in videos. For this, the proposed framework employs an ImageNet pre-trained convolutional neural network (CNN) model known as EfficientNet-B7 to extract video descriptors, which are then fed to bidirectional long short-term memory (BiLSTM) network to learn effective video representations and perform multiclass video classification. An attention mechanism is also integrated after BiLSTM to apply attentio