The Evolution of Digital Image Forgery Detection: A Comprehensive Bibliometric Review, Trends, and Future Prospects

ABSTRACT Ensuring the authenticity of digital images is essential in forensic investigations, media, and scientific research, where these images serve as critical evidence. This necessity leads to the development of digital image forgery detection (DIF). This study reviewed DIF articles from 2005 to 2024. It performs a comprehensive evaluation and bibliometric analysis of DIF methodologies, aiming to uncover trends, technological advancements, and thematic progressions. The study utilized Scopus data to illustrate key DIF methodologies, citation trends, and thematic changes. It offers fresh insights by showcasing the rising prevalence of deep learning‐based DIF techniques post‐2018 and the emergence of hybrid models that integrate traditional and AI‐driven methods to bolster detection robustness and precision. The review highlights that China and the United States are leading the field, with significant contributions from institutions such as the South China University of Technology and the State University of New York at Albany. The bibliometric analysis reveals three key trends: a marked increase in deep learning‐based DIF methods since 2018, indicating a shift away from traditional feature‐based techniques; strengthening collaboration between industry and academia, especially in China and the US, fueling significant advancements; and a heightened focus on real‐world forgeries, such as deepfakes, emphasizing the necessity for more adaptable detection tools. It highlights challenges like the lack of substantial and varied benchmark datasets. This comprehensive study also suggests enhancing DIF accuracy and applicability across different domains. This article is categorized under: Algorithmic Development > Multimedia Technologies > Computational Intelligence