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May 31, 2026The Journal of Korean Institute of Information Technology

Implementation of a Multimodal Integrated Detection Framework for Deepfake Videos and Exaggerated Advertisements

Tehhao Teng Jueun Han Chaerim Lim Hyunho Park Subin Ahn Daeun Lee Gilsang Yoo

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Key Points

The aim is to create a detection system for deepfake videos and exaggerated advertisements by integrating video and text analysis.
Developed a multimodal detection system combining video and text analysis for online content.
Used a hybrid model of MobileNetV3 and LSTM for deepfake detection, and KcELECTRA for analyzing advertisement exaggeration.
Classified advertisement risk levels based on integrated analysis of both components.
Achieved a deepfake detection accuracy of 0.8833.
Obtained an accuracy of 0.9748 for detecting exaggerated advertisements.
The integrated approach enhances consumer trust against rising online advertisement threats.

Implication

Demonstrates improved detection of deepfakes and exaggerated ads, suggesting enhanced consumer protection.

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Cite This Study

Teng et al. (2026) studied this question.

synapsesocial.com/papers/6a362e1edb0793dc1a535fa5 https://doi.org/10.14801/jkiit.2026.24.5.201

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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