Authors:
Jarosław Bernacki
and
Rafał Scherer
Affiliation:
Department of Intelligent Computer Systems, Czȩstochowa University of Technology, al. Armii Krajowej 36, 42-200 Czȩstochowa, Poland
Keyword(s):
Digital Camera Identification, Sensor Identification, Digital Forensics, Privacy, Security, Machine Learning, Deep Models, Convolutional Neural Networks.
Abstract:
In this paper, we address the challenge of digital camera identification within the realm of digital forensics. While numerous algorithms leveraging camera fingerprints exist, few offer both speed and accuracy, particularly in the context of modern high-resolution digital cameras. Moreover, the storage requirements for these fingerprints, often represented as matrices corresponding to the original image dimensions, pose practical challenges for forensic centers. To tackle these issues, we propose a novel approach utilizing a convolutional autoencoder (AE) to generate compact representations of camera fingerprints. Our method aims to balance accuracy with efficiency, facilitating rapid and reliable identification across a range of cameras and image types. Extensive experimental evaluation demonstrates the effectiveness of our approach, showcasing its potential for practical deployment in forensic scenarios. By providing a streamlined method for camera identification, our work contribu
tes to advancing the capabilities of digital forensic analysis.
(More)