dorsal/arxiv
View SchemaFrom Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum
| Authors | Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang |
|---|---|
| Categories | |
| ArXiv ID | 2601.06368vv1 |
| URL | https://arxiv.org/abs/2601.06368 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
To improve the quality of Differentially private (DP) synthetic images, most studies have focused on improving the core optimization techniques (e.g., DP-SGD). Recently, we have witnessed a paradigm shift that takes these techniques off the shelf and studies how to use them together to achieve the best results. One notable work is DP-FETA, which proposes using `central images' for `warming up' the DP training and then using traditional DP-SGD. Inspired by DP-FETA, we are curious whether there are other such tools we can use together with DP-SGD. We first observe that using `central images' mainly works for datasets where there are many samples that look similar. To handle scenarios where images could vary significantly, we propose FETA-Pro, which introduces frequency features as `training shortcuts.' The complexity of frequency features lies between that of spatial features (captured by `central images') and full images, allowing for a finer-grained curriculum for DP training. To incorporate these two types of shortcuts together, one challenge is to handle the training discrepancy between spatial and frequency features. To address it, we leverage the pipeline generation property of generative models (instead of having one model trained with multiple features/objectives, we can have multiple models working on different features, then feed the generated results from one model into another) and use a more flexible design. Specifically, FETA-Pro introduces an auxiliary generator to produce images aligned with noisy frequency features. Then, another model is trained with these images, together with spatial features and DP-SGD. Evaluated across five sensitive image datasets, FETA-Pro shows an average of 25.7% higher fidelity and 4.1% greater utility than the best-performing baseline, under a privacy budget $\epsilon = 1$.
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"date_created": "2026-02-17T05:53:07.568000Z",
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"abstract": "To improve the quality of Differentially private (DP) synthetic images, most studies have focused on improving the core optimization techniques (e.g., DP-SGD). Recently, we have witnessed a paradigm shift that takes these techniques off the shelf and studies how to use them together to achieve the best results. One notable work is DP-FETA, which proposes using `central images\u0027 for `warming up\u0027 the DP training and then using traditional DP-SGD.\n Inspired by DP-FETA, we are curious whether there are other such tools we can use together with DP-SGD. We first observe that using `central images\u0027 mainly works for datasets where there are many samples that look similar. To handle scenarios where images could vary significantly, we propose FETA-Pro, which introduces frequency features as `training shortcuts.\u0027 The complexity of frequency features lies between that of spatial features (captured by `central images\u0027) and full images, allowing for a finer-grained curriculum for DP training. To incorporate these two types of shortcuts together, one challenge is to handle the training discrepancy between spatial and frequency features. To address it, we leverage the pipeline generation property of generative models (instead of having one model trained with multiple features/objectives, we can have multiple models working on different features, then feed the generated results from one model into another) and use a more flexible design. Specifically, FETA-Pro introduces an auxiliary generator to produce images aligned with noisy frequency features. Then, another model is trained with these images, together with spatial features and DP-SGD. Evaluated across five sensitive image datasets, FETA-Pro shows an average of 25.7% higher fidelity and 4.1% greater utility than the best-performing baseline, under a privacy budget $\\epsilon = 1$.",
"arxiv_id": "2601.06368",
"authors": [
"Chen Gong",
"Kecen Li",
"Zinan Lin",
"Tianhao Wang"
],
"categories": [
"cs.CR",
"cs.CV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "From Easy to Hard++: Promoting Differentially Private Image Synthesis Through Spatial-Frequency Curriculum",
"url": "https://arxiv.org/abs/2601.06368",
"version": "v1"
},
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