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Article

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

1
Université de Lorraine, CNRS, LORIA, F-54000 Nancy, France
2
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Irene Amerini
J. Imaging 2021, 7(2), 33; https://doi.org/10.3390/jimaging7020033
Received: 20 December 2020 / Revised: 29 January 2021 / Accepted: 8 February 2021 / Published: 11 February 2021
(This article belongs to the Special Issue Image and Video Forensics)
The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs. View Full-Text
Keywords: camera fingerprint; smartphone identification; user profile linking; digital investigations; social network; classification camera fingerprint; smartphone identification; user profile linking; digital investigations; social network; classification
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MDPI and ACS Style

Rouhi, R.; Bertini, F.; Montesi, D. No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway. J. Imaging 2021, 7, 33. https://doi.org/10.3390/jimaging7020033

AMA Style

Rouhi R, Bertini F, Montesi D. No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway. Journal of Imaging. 2021; 7(2):33. https://doi.org/10.3390/jimaging7020033

Chicago/Turabian Style

Rouhi, Rahimeh, Flavio Bertini, and Danilo Montesi. 2021. "No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway" Journal of Imaging 7, no. 2: 33. https://doi.org/10.3390/jimaging7020033

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