Video authentication detection using deep learning: a systematic literature review

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.