Securing Digital Evidence at Source: At-Capture Camera Fingerprinting to Combat Illicit Trade

This study explores the potential of sensor-based camera fingerprinting to strengthen digital image authentication and address vulnerabilities in existing verification systems. Focusing on Sensor Pattern Noise (SPN), particularly Photo-Response Non-Uniformity (PRNU), it examines how microscopic imperfections in image sensors can help establish the source and integrity of digital photographs. The study highlights potential applications across high-risk sectors, including passport verification, insurance fraud detection and investigations of illicit online markets. It also considers how deep learning can improve fingerprint detection while recognising the limitations introduced by image compression, smartphone processing and deliberate manipulation. By examining the opportunities, technical constraints and implementation requirements of camera fingerprinting, the study contributes to discussions on digital forensic technologies and their role in strengthening authentication, accountability and fraud prevention.