AnimeNet: A Deep Learning Approach for Detecting Violence and Eroticism in Animated Content

Abstract

Cartoons serve as significant sources of entertainment for children and adolescents. However, numerous animated videos contain unsuitable content, such as violence, eroticism, abuse, and vehicular accidents. Current content detection methods rely on manual inspection, which is resource-intensive, time-consuming, and not always reliable. Therefore, more efficient detection methods are necessary to safeguard young viewers. This paper addresses this significant problem by proposing a novel deep learning-based system, AnimeNet, designed to detect varying degrees of violent and erotic content in videos. AnimeNet utilizes a novel Convolutional Neural Network (CNN) model to extract image features effectively, classifying violent and erotic scenes in videos and images. The novelty of the work lies in the introduction of a novel channel-spatial attention module, enhancing the feature extraction performance of the CNN model, an advancement over previous efforts in the literature. To validate the approach, I compared AnimeNet with state-of-the-art classification methods, including ResNet, RegNet, ConvNext, ViT, and MobileNet. These were used to identify violent and erotic scenes within specific video frames. The results showed that AnimeNet outperformed these models, proving it to be well-suited for real-time applications in videos or images. This work presents a significant leap forward in automatic content detection in animation, offering a high-accuracy solution that is less resource-intensive and more reliable than current methods. The proposed approach enables it possible to better protect young audiences from exposure to unsuitable content, underlining its importance and potential for broad social impact.