Research Article
Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures
@INPROCEEDINGS{10.1007/978-3-031-77075-3_20, author={S Kumar Reddy Mallidi and Sujana Bellapukonda and Nikhitha Gollapudi and Karthik Malaka and P. Sreeshanth and Sai Sri Harsha Polisetti}, title={Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures}, proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I}, proceedings_a={IC4S}, year={2025}, month={2}, keywords={Deep Learning Convolutional Neural Networks (CNNs) Transfer Learning Child Safety Content Moderation}, doi={10.1007/978-3-031-77075-3_20} }- S Kumar Reddy Mallidi
Sujana Bellapukonda
Nikhitha Gollapudi
Karthik Malaka
P. Sreeshanth
Sai Sri Harsha Polisetti
Year: 2025
Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures
IC4S
Springer
DOI: 10.1007/978-3-031-77075-3_20
Abstract
This study introduces a deep learning solution to safeguard children from potentially harmful content in cartoons, such as violence and adulterous scenes. Recognizing the possible negative influence of such content on young minds, we developed a hybrid Convolutional Neural Network (CNN) model that combines the strengths of InceptionV3 and VGG16. This model is meticulously designed to effectively identify and filter out aggressive and inappropriate content. Our comparative analysis highlights the proposed model’s superior performance, including established models like VGG16, VGG19, ResNet50, and InceptionV3. It is particularly notable for its high efficiency and fewer parameters, successfully addressing the common issue of overfitting often encountered in deep learning models. This advancement in performance is critical for the model's ability to identify and mitigate exposure to harmful content in cartoons accurately. The results of this research represent a significant advancement in content moderation technology for children's media, paving the way for a safer and more suitable viewing environment.
