A Review of JPEG File Carving: Challenges, Techniques, and Future Directions

Abstract

JPEG file carving is an essential component of digital forensics, enabling the recovery of image files from storage devices where metadata is missing or corrupted. This review explores the evolution of JPEG file carving techniques, from traditional header/footer methods to advanced approaches leveraging machine learning, genetic algorithms, and hybrid systems. The study highlights the challenges associated with file fragmentation, metadata loss, and the complexities of modern storage systems, emphasizing the limitations of existing tools in addressing these issues. Emerging methodologies, such as entropy clustering, context-aware carving, and deep learning for automated validation, demonstrate significant potential for improving recovery accuracy and scalability. By examining these advancements, the review identifies critical research gaps and proposes future directions, including the development of real-time AI-based tools and standardized evaluation frameworks. The findings underscore the importance of continued innovation in JPEG file carving, ensuring that digital forensics remains effective in addressing the growing complexities of data recovery and cyber investigations.