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January 1, 2026International Journal of Computational Science and Engineering

SMA-CLMPNet: spatial multiscale attention enabled convolutional distributed memory network for intra-frame video forgery detection

Neha Dhiman Hakam Singh Abhishek Abhishek

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Key Points

The research aims to develop an effective model for detecting forgery in video frames using advanced neural network techniques.
Developed SMA-CLMPNet architecture utilizing spatial multiscale attention and distributed memory mechanisms.
Conducted experiments on a dataset of forged videos to evaluate detection accuracy and performance.
Analyzed results using statistical methods to determine effectiveness compared to traditional models.
Achieved an accuracy of 95% indicating a significant improvement over conventional detection methods.
Reported a false positive rate of 2% with a sensitivity of 93%, demonstrating robust performance in real-world applications.

Implication

Demonstrates an approach for video forgery detection, suggesting improved accuracy and efficiency.

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Cite This Study

Dhiman et al. (2026) studied this question.

synapsesocial.com/papers/6a11475048a409a3a49e0499 https://doi.org/10.1504/ijcse.2026.10078605

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