Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset
Quick Answer
This study analyzes the quality of synthetic images compared to real images using the CIFAKE dataset, focusing on feature space, color statistics, and training processes.
Quick Take
This study analyzes the quality of synthetic images compared to real images using the CIFAKE dataset, focusing on feature space, color statistics, and training processes. It proposes a strategy for evaluating synthetic images to enhance the reliability of image classification models, addressing the growing need for training data in high-performance models.
Key Points
- Synthetic data is increasingly used to supplement real image shortages in training.
- The study evaluates synthetic images from different generation methods against real images.
- Three analysis perspectives include high-dimensional features, low-level color statistics, and training processes.
- A strategy for safe incorporation of synthetic data into training is proposed.
- Research aims to improve the reliability and safety of image classification models.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of training data to improve performance, securing sufficient real images is often impractical. As a means to compensate for this shortage, the use of synthetic data is becoming widespread. However, synthetic images are not necessarily equivalent to real images for training purposes. This study systematically analyzes the differences between two types of synthetic images created by different generation methods and real images from three perspectives: high-dimensional feature space, low-level statistics in color space, and the model training process. Furthermore, it experimentally verifies how synthetic data should be utilized by considering realistic data mixing scenarios. This enables the proposal of an evaluation and application strategy for performing preliminary assessments on synthetic images of unknown quality and safely incorporating them into training. This research aims to contribute to enhancing the reliability and safety of image classification models utilizing synthetic images.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.12165 [cs.CV] |
| (or arXiv:2607.12165v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.12165 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kuniko Paxton [view email]
[v1]
Mon, 13 Jul 2026 21:18:15 UTC (4,711 KB)
— Originally published at arxiv.org
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