File Carving for Digital Forensics: Moving Beyond Conventional Methods to AI-Powered Solutions

Conventional file carving techniques mostly rely on heuristic and signature-based techniques, which have drawbacks when handling complicated storage structures and severely splintered data. Latest advances in file carving based on machine learning offer encouraging possibilities by increasing the accuracy of reconstruction, information categorization, and recognition of patterns. The effectiveness of cutting-edge machine learning approaches used in file carving is examined in this review, and it additionally investigates at how well they work for extracting features, model classification, and deep learning-based pattern reconstruction. The study intends to offer a thorough grasp of how machine learning approaches could aid conventional forensic procedures, unlocking the door to further sophisticated and effective data retrieval approaches.