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Authors: Georg Wimmer ; Dominik Söllinger and Andreas Uhl

Affiliation: Paris Lodron University Salzburg, Jakob-Haringer-Str. 2, 5020 Salzburg, Austria

Keyword(s): Image Synthesis, GAN, Diffusion Model, Synthetic Artifacts.

Abstract: Deep learning based methods require large amounts of annotated training data. Using synthetic images to train deep learning models is a faster and cheaper alternative to gathering and manually annotating training data. However, synthetic images have been demonstrated to exhibit a unique model-specific fingerprint that is not present in real images. In this work, we investigate the effect of such model-specific traces on the training of CNN-based classifiers. Two different methods are applied to generate synthetic training data, a conditional GAN-based image-to-image translation method (BicycleGAN) and a conditional diffusion model (Palette). Our results show that CNN-based classifiers can easily be fooled by generator-specific traces contained in synthetic images. As we will show, classifiers can learn to discriminate based on the traces left by the generator, instead of class-specific features.

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Paper citation in several formats:
Wimmer, G.; Söllinger, D. and Uhl, A. (2024). The Risk of Image Generator-Specific Traces in Synthetic Training Data. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 199-206. DOI: 10.5220/0012420600003660

@conference{visapp24,
author={Georg Wimmer. and Dominik Söllinger. and Andreas Uhl.},
title={The Risk of Image Generator-Specific Traces in Synthetic Training Data},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={199-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012420600003660},
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 3: VISAPP
TI - The Risk of Image Generator-Specific Traces in Synthetic Training Data
SN - 978-989-758-679-8
IS - 2184-4321
AU - Wimmer, G.
AU - Söllinger, D.
AU - Uhl, A.
PY - 2024
SP - 199
EP - 206
DO - 10.5220/0012420600003660
PB - SciTePress

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