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Open Access

ARTICLE

Multi-CNN Fusion Framework for Predictive Violence Detection in Animated Media

Tahira Khalil1, Sadeeq Jan2,*, Rania M. Ghoniem3, Muhammad Imran Khan Khalil1
1 Department of Computer Science & Information Technology, University of Engineering & Technology, Peshawar, 25000, Pakistan
2 National Center for Cyber Security, University of Engineering & Technology, Peshawar, 25000, Pakistan
3 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
* Corresponding Author: Sadeeq Jan. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072655

Received 01 September 2025; Accepted 06 November 2025; Published online 02 December 2025

Abstract

The contemporary era is characterized by rapid technological advancements, particularly in the fields of communication and multimedia. Digital media has significantly influenced the daily lives of individuals of all ages. One of the emerging domains in digital media is the creation of cartoons and animated videos. The accessibility of the internet has led to a surge in the consumption of cartoons among young children, presenting challenges in monitoring and controlling the content they view. The prevalence of cartoon videos containing potentially violent scenes has raised concerns regarding their impact, especially on young and impressionable minds. This article contributes to the growing concerns about the impact of animated media on children’s mental health and offers solutions to help mitigate these effects. To address this issue, an intelligent, multi–CNN fusion framework is proposed for detecting and predicting violent content in upcoming frames of animated videos. The framework integrates probabilistic and deep learning methodologies by leveraging a combination of visual and temporal features for violence prediction in future scenes. Two specific convolutional neural network classifiers i.e., VGG16 and ResNet18 are employed to classify scenes from animated content as violent or non-violent. To enhance decision robustness, this study introduces a fusion strategy based on weighted averaging, combining the outputs of both Convolutional Neural Networks (CNNs) into a single decision stream. The resulting classifications are subsequently fed into Naive Bayes classifier, which analyzes sequential patterns to forecast violence in future scenes. The experimental findings demonstrate that the proposed framework achieved predictive accuracy of 92.84%, highlighting its effectiveness for intelligent content moderation. These results underscore the potential of intelligent data fusion techniques in enhancing the reliability and robustness of automated violence detection systems in animated content. This framework offers a promising solution for safeguarding young audiences by enabling proactive and accurate moderation of animated videos.

Keywords

Violence prediction; multi-model fusion; cartoon videos; residual network (ResNet); visual geometry group (VGG); CNN

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