Abstract
Recent advancements in deep learning have notably influenced research across various data types, with a significant focus on video authentication. This area has emerged as a crucial aspect of ensuring the integrity and trustworthiness of video content amidst growing concerns over manipulation and falsification. It is emerging as a field ripe for exploration. This paper presents a systematic literature review (SLR) on using deep learning techniques for video authentication, addressing the urgent need for robust methods to verify video integrity amidst increasing manipulation threats. Reviewing literature from the past five years, this SLR reviews 99 research articles from the last five years and highlights the significant progress made through deep learning techniques (Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), and Generative Adversarial Networks (GANs)). It aims to investigate applications, techniques, datasets, and challenges in video authentication, providing a comprehensive guide for researchers. This study encompasses a broad range of research articles, identifying key advancements and trends in combating video manipulation and focusing on maintaining digital media trustworthiness.
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All articles reviewed are publicly available from the databases listed in the methodology section. Full references are provided in the reference list.
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Acknowledgements
The authors thank the Ministry of Higher Education, Malaysia, for funding support Fundamental Research Grant Scheme FRGS/1/2019/ICT02/UKM/02/9. A special appreciation to Cybersecurity Malaysia for collaborating with Universiti Kebangsaan Malaysia (UKM) under a research grant TT-2024-010.
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Ayat and Asyikin developed the search strategy; Ayat and Sarah screened the articles and extracted the data; Ayat and Dr. Hafizah conducted the quality assessment; Ayat drafted the manuscript; Dr. Huda reviewed and edited the manuscript for critical content.
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This systematic review synthesizes data from previously published studies; therefore, no primary ethical approval or informed consent was required. All analysed studies were assessed for ethical standards and quality.
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this systematic review.
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Alrawahneh, A.AM., Abdullah, S.N.A.S., Abdullah, S.N.H.S. et al. Video authentication detection using deep learning: a systematic literature review. Appl Intell 55, 239 (2025). https://doi.org/10.1007/s10489-024-05997-8
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DOI: https://doi.org/10.1007/s10489-024-05997-8