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Digital camera identification by fingerprint’s compact representation

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Abstract

In this paper we deal with the issue of digital camera identification (DCI) based on images. This area matches the digital forensics (DF) research. This topic has attracted many researchers and number of algorithms for DCI have been proposed. However, majority of them focus only on camera identification with high accuracy without taking into account the speed of image processing. In this paper we propose an effective algorithm for much faster camera identification than state-of-the-art algorithms. Experimental evaluation conducted on two large image datasets including almost 14.000 images confirms that the proposed algorithm achieves high classification accuracy of 97 [%] in much shorter time compared with state-of-the-art algorithms which obtained 92.0 − 96.0 [%]. We also perform a statistical analysis of obtained results which confirms their reliability.

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  1. Submitted, to be published soon. Draft of the web page: https://kisi.pcz.pl/imagine/

  2. http://www.optyczne.pl

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Acknowledgements

The authors would like to thank the Editorial Office of Optyczne.plFootnote 2 website for sharing part of images utilized in Dataset I.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Correspondence to Jarosław Bernacki.

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Appendix:

Appendix:

Due to a large number of confusion matrices, we present them in this Section.

1.1 Experiment I

Results for the brand recognition of Experiment I are presented as Tables 2021 and 22 (for Dataset I) and Tables 2324 and 25 (for Dataset II—Dresden Image Database).

Table 20 Experiment I. Confusion matrix of brand recognition, CompaRe algorithm, ACC = 97.0 [%], Dataset I
Table 21 Experiment I. Confusion matrix of brand recognition, Valsesia et al.’s algorithm, ACC = 96.0 [%], Dataset I
Table 22 Experiment I. Confusion matrix of brand recognition, Li et al.’s algorithm, ACC = 95.0 [%], Dataset I
Table 23 Experiment I. Confusion matrix of brand recognition, CompaRe algorithm, ACC = 96.0 [%], Dataset II (Dresden Image Database)
Table 24 Experiment I. Confusion matrix of brand recognition, Valsesia et al.’s algorithm, ACC = 95.0 [%], Dataset II (Dresden Image Database)
Table 25 Experiment I. Confusion matrix of brand recognition, Li et al.’s algorithm, ACC = 94.0 [%], Dataset II (Dresden Image Database)

1.2 Experiment II

Results for the brand recognition of Experiment I are presented as Tables 2627282930 and 31 (for Dataset I) and Tables 3233343536 and 37 (for Dataset II—Dresden Image Database).

Table 26 Experiment II. Confusion matrix of brand recognition, CompaRe algorithm, ACC = 97.0 [%], Dataset I
Table 27 Experiment II. Confusion matrix of brand recognition, Lukás et al.’s algorithm, ACC = 96.0 [%], Dataset I
Table 28 Experiment II. Confusion matrix of brand recognition, Bondi et al.’s algorithm, ACC = 95.0 [%], Dataset I
Table 29 Experiment II. Confusion matrix of brand recognition, Tuama et al.’s algorithm, ACC = 94.0 [%], Dataset I
Table 30 Experiment II. Confusion matrix of brand recognition, Mandelli et al.’s algorithm, ACC = 94.0 [%], Dataset I
Table 31 Experiment II. Confusion matrix of brand recognition, Kirchner & Johnson, ACC = 92.0 [%], Dataset I
Table 32 Experiment II. Confusion matrix of brand recognition, CompaRe algorithm, ACC = 97.0 [%], Dataset II (Dresden Image Database)
Table 33 Experiment II. Confusion matrix of brand recognition, Lukás et al.’s algorithm, ACC = 96.0 [%], Dataset II (Dresden Image Database)
Table 34 Experiment II. Confusion matrix of brand recognition, Bondi et al.’s algorithm, ACC = 94.0 [%], Dataset II (Dresden Image Database)
Table 35 Experiment II. Confusion matrix of brand recognition, Tuama et al.’s algorithm, ACC = 94.0 [%], Dataset II (Dresden Image Database)
Table 36 Experiment II. Confusion matrix of brand recognition, Mandelli et al.’s algorithm, ACC = 94.0 [%], Dataset II (Dresden Image Database)
Table 37 Experiment II. Confusion matrix of brand recognition, Kirchner & Johnson algorithm, ACC = 92.0 [%], Dataset II (Dresden Image Database)

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Bernacki, J. Digital camera identification by fingerprint’s compact representation. Multimed Tools Appl 81, 21641–21674 (2022). https://doi.org/10.1007/s11042-022-12468-0

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