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Authors: Venkata Mannam ; Andrey Makrushin and Jana Dittmann

Affiliation: Department of Computer Science, Otto von Guericke University, Magdeburg, Germany

Keyword(s): Watermarking, Biometrics, Synthetic Fingerprints, Synthetic Data Detection, GAN, Pix2pix.

Abstract: Due to the rise of high-quality synthetic data produced by generative models and a growing mistrust in images published in social media, there is an urgent need for reliable means of synthetic image detection. Passive detection approaches cannot properly handle images created by ”unknown” generative models. Embedding watermarks in synthetic images is an active detection approach which transforms the task from fake detection to watermark extraction. The focus of our study is on watermarking biometric fingerprint images produced by Generative Adversarial Networks (GAN). We propose to watermark images used for training of a GAN model and study the interplay between the watermarking algorithm, GAN architecture, and training hyperparameters to ensure the watermark transfer from training data to GAN-generated fingerprint images. A hybrid watermarking algorithm based on discrete cosine transformation, discrete wavelet transformation, and singular value decomposition is shown to produce tran sparent logo watermarks which are robust to pix2pix network training. The pix2pix network is applied to reconstruct realistic fingerprints from minutiae. The watermark imperceptibility and robustness to GAN training are validated by peak signal-to-noise ratio and bit error rate respectively. The influence of watermarks on reconstruction success and realism of fingerprints is measured by Verifinger matching scores and NFIQ2 scores respectively. (More)

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Paper citation in several formats:
Mannam, V.; Makrushin, A. and Dittmann, J. (2024). On Feasibility of Transferring Watermarks from Training Data to GAN-Generated Fingerprint Images. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 435-445. DOI: 10.5220/0012418100003660

@conference{visapp24,
author={Venkata Mannam. and Andrey Makrushin. and Jana Dittmann.},
title={On Feasibility of Transferring Watermarks from Training Data to GAN-Generated Fingerprint Images},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={435-445},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012418100003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - On Feasibility of Transferring Watermarks from Training Data to GAN-Generated Fingerprint Images
SN - 978-989-758-679-8
IS - 2184-4321
AU - Mannam, V.
AU - Makrushin, A.
AU - Dittmann, J.
PY - 2024
SP - 435
EP - 445
DO - 10.5220/0012418100003660
PB - SciTePress

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