Forensics AI: Next-Gen Digital Forensics with AI
K Vemuri Chandana,
Dudekula Abdul Salam,
Adoni Althaf,
Komme Kavya Sree,
Degalpadu Afrid
Crime Scene Investigation (CSI) has become increasingly challenging due to the massive growth of digital
evidence across devices, cloud platforms, and online activities. Traditional digital forensic methods
are slow, manually intensive, and prone to human error, making them ineffective for handling large
and complex datasets. The proposed system introduces an AI-integrated digital forensic framework that
applies Machine Learning, Deep Learning, Natural Language Processing (NLP), and Computer Vision
to automate key forensic tasks. These include anomaly detection in log files, rapid text classification, and
object or scene identification in images and videos. The framework follows a human-in-the-loop model,
ensuring AI handles data processing while experts make final decisions, maintaining accuracy and legal
admissibility. By integrating Machine Learning, Deep Learning, NLP, and Computer Vision, the system
helps investigators identify patterns, anomalies, and critical evidence quickly.
