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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65170

Deep Learning Based Detection Of Violent Scenes In Cartoon Movies

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65170

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65170

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Tejes Kanhere

Tejes Kanhere

Information Technology

Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology

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Shubham Suwarnakar

Shubham Suwarnakar

Information Technology

Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology

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Keshav Bhagwat

Keshav Bhagwat

Information Technology

Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology

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Abstract

Children are now much more exposed to animated content with violent sequences including fights, explosions, shoot-ings, and bloodshed due to the quick development of digital entertainment platforms. Children’s emotional and psychological conduct may be adversely affected by ongoing exposure to such damaging content. As a result, automatic identification of violent scenes in cartoon videos has grown in importance as a field of study for deep learning and computer vision. Using pretrained MobileNetV2 and Long Short-Term Memory (LSTM) networks, this study suggests an effective and lightweight approach for detecting cartoon violence. While the LSTM net-work records temporal connections between successive video frames for precise violence recognition, the suggested approach uses MobileNetV2 to extract deep spatial characteristics from cartoon frames using transfer learning. Video preprocessing, keyframe extraction, feature extraction, temporal learning, and multi-class violence classification are all part of the methodology.

KEYWORDS

Violence Detection Cartoon Video Analy-sis Deep Learning MobileNetV2 Long Short-Term Memory (LSTM) Transfer Learning Video Classification Computer Vision Artificial Intelligence Keyframe Extraction Content Filtering Animated Scene Recognition.

Cite This Article

Tejes Kanhere, Shubham Suwarnakar, Keshav Bhagwat, "Deep Learning Based Detection Of Violent Scenes In Cartoon Movies," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026. https://doi.org/10.55041/IJSREM65170

Full Article/Conclusion

The MobileNetV2-LSTM Framework that has been sug-gested will be capable of identifying scenes of violence in cartoon videos. The Framework has been tested with respect to accuracy, generalization and computational efficiency. The Framework will also help to moderate content and use filters associated with/controlled by the parent. Future work may include: real time use; and more accurate identifying through use of multiple sources of media (i.e. text, audio, video).

References

[1]     N. F. Khan, S. U. Amin, Z. Jan and C. Yan, ”Detection of Violent Scenes in Cartoon Movies Using a Deep Learning Approach,” in IEEE Access, vol. 12, pp. 154080-154091, 2024 [2]     Q. Zhang, Z. Duan, D. Xiang, Y. Yu, and J. Tian, “The effects of prosocial cartoon examples on children’s donating behavior,” Psychol. Res. Behav. Manage., vol. 14, pp. 1257–1268, Aug. 2021. [3]     S. Gon¸calves, R. Ferreira, E. M. Concei¸c˜ao, C. Silva, P. P. P. Machado,E. Boyland, and A. Vaz, “The impact of exposure to cartoons promoting healthy eating on children’s food preferences and choices,” J. Nutrition Edu. Behav., vol. 50, no. 5, pp. 451 457, May 2018. [4]     A. Drummond, J. D. Sauer, C. J. Ferguson, P. R. Cannon, and L. C. Hall, “Violent and non violent virtual reality video games: Influences on affect, aggressive cogni tion, and aggressive behavior. Two pre registered experiments,” J. Experim. Social Psychol., vol. 95, Jul. 2021, Art. no. 104119. [5]     F. Craig, F. Tenuta, A. De Giacomo, A. Trabacca, and A. Costabile, “A systematic review of problematic video gameuseinpeoplewithautism spectrum disorders,” Res. Autism Spectr. Disorders, vol. 82, Apr. 2021, Art. no. 101726. [6]     F. Parvin and S. Islam, “The impact of cartoon programs on children’s physical health, intelligence, behavior and activities,” Eur. J. Physiother apy Rehabil. Stud., vol. 1, no. 1, pp. 647–648, 2020. [7]     P. Zhou, Q. Ding, H. Luo, and X. Hou, “Violent interaction detection in video based on deep learning,” J. Phys., Conf. Ser., vol. 844, Jun. 2017, Art. no. 012044. [8]     M. Khan, M. A. Tahir, and Z. Ahmed, “Detection of violent content in cartoon videos using multimedia content detection techniques,” in Proc. IEEE 21st Int. Multi Topic Conf. (INMIC), Nov. 2018, pp. 1–5. [9]     A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., vol. 25, 2012, pp. 1097–1105. [10]    Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

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Publication Date

6/19/2026

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Review Rounds: 2

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