Unmasking Frame Duplication: Comprehensive Approach to Detection in Digital Video Using Machine Learning Algorithms and Signal Processing Techniques

Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction....

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Bibliographic Details
Published in2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) pp. 1 - 6
Main Authors N, Praddipraj N, B, Praveen Kumar, S, Sreeji
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.04.2024
Subjects
Accuracy
Data engineering
Digital Video
Duplication
Feature extraction
Filtering
Machine learning
Machine learning algorithms
Noise reduction
Resolution
Signal processing
Unmasking
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Summary:Our research proposes a comprehensive approach to identify duplicate frames in digital videos. It integrates machine learning and signal processing techniques for effective identification. The process begins with pre-processing, enhancing video quality through temporal filtering and noise reduction. Next, feature extraction captures statistical and structural information from each frame. Deep neural networks and other machine learning methods are used to classify frames as original or duplicated using these features. To enhance detection accuracy, signal processing methods like block-based and key point-based approaches are applied. Block-based methods divide frames into segments for region comparison, while key point-based methods identify and match key points between frames. Our method's effectiveness and robustness are demonstrated through experiments on various digital videos. This combination of machine learning and signal processing offers a comprehensive solution to uncover frame duplication, ensuring video content's integrity and authenticity.
DOI:10.1109/ADICS58448.2024.10533589