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Authors: Jarosław Bernacki and Rafał Scherer

Affiliation: Department of Intelligent Computer Systems, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

Keyword(s): Digital Camera Identification, Sensor Identification, Digital Forensics, Privacy, Security, Machine Learning, Deep Models, Convolutional Neural Networks.

Abstract: We present the IMAGINE dataset. The proposed dataset may be used for benchmarking digital camera identification algorithms, which is an important issue in the field of digital forensics. So far, the most common image dataset seems to be the Dresden Image Database, but this dataset contains images from relatively old devices which include charge-coupled device (CCD) imaging sensors. Our dataset contains a number of images coming from modern devices which include mobile devices, compact cameras, and digital single-lens reflex/mirrorless (DSLR/DSLM) with Complementary Metal-Oxide-Semiconductor (CMOS) imaging sensors. Extensive experimental evaluation performed on a set of modern camera identification methods and algorithms confirmed the reliability of the IMAGINE dataset.

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Paper citation in several formats:
Bernacki, J. and Scherer, R. (2023). IMAGINE Dataset: Digital Camera Identification Image Benchmarking Dataset. In Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-666-8; ISSN 2184-7711, SciTePress, pages 799-804. DOI: 10.5220/0012130300003555

@conference{secrypt23,
author={Jarosław Bernacki. and Rafał Scherer.},
title={IMAGINE Dataset: Digital Camera Identification Image Benchmarking Dataset},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT},
year={2023},
pages={799-804},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012130300003555},
isbn={978-989-758-666-8},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT
TI - IMAGINE Dataset: Digital Camera Identification Image Benchmarking Dataset
SN - 978-989-758-666-8
IS - 2184-7711
AU - Bernacki, J.
AU - Scherer, R.
PY - 2023
SP - 799
EP - 804
DO - 10.5220/0012130300003555
PB - SciTePress