When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training

arXiv:2607.13541 · cs.CR, cs.LG · Submitted 2026-07-15 · Read on arXiv

Na Li, Boyu Kuang, Hongsheng Hu, Liquan Chen, Hyoungshick Kim, Yansong Gao, Anmin Fu

cs.CR, cs.LG

Submitted: 2026-07-15

Code: https://github.com/TencentARC/FluxKits

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Accuracy — 34× in this paper · explained in 1 episode
AUC — 26× in this paper · explained in 2 episodes
Diffusion Model — 11× in this paper · explained in 8 episodes
Diffusion Models — 11× in this paper · explained in 6 episodes
Fine-Tuning — 8× in this paper · explained in 5 episodes
Transformer — 8× in this paper · explained in 2 episodes
Core — 7× in this paper · explained in 2 episodes
Benchmark — 6× in this paper · explained in 2 episodes
Synthetic Data Generation — 6× in this paper · explained in 5 episodes
Foundation Model — 5× in this paper · explained in 3 episodes
Generative Model — 5× in this paper · explained in 2 episodes
Generative Models — 5× in this paper · explained in 2 episodes
Distillation — 4× in this paper · explained in 2 episodes
IEEE Symposium on Security and Privacy — 4× in this paper
Adam — 3× in this paper · explained in 2 episodes
Differential Privacy — 3× in this paper · explained in 4 episodes
Fidelity — 3× in this paper · explained in 2 episodes
Robustness — 3× in this paper · explained in 5 episodes
Adaptation — 2× in this paper · explained in 1 episode
Calibration — 2× in this paper · explained in 7 episodes
Foundation Models — 2× in this paper · explained in 7 episodes
Latent Diffusion Model — 2× in this paper · explained in 2 episodes
Latent Diffusion Models — 2× in this paper · explained in 2 episodes
Latent Space — 2× in this paper · explained in 7 episodes
Machine Unlearning — 2× in this paper · explained in 2 episodes
Real-Synthetic Mix-Training — 2× in this paper
Actor — 1× in this paper · explained in 3 episodes
Adversarial Examples — 1× in this paper · explained in 2 episodes
Alignment — 1× in this paper · explained in 1 episode
Benchmarking — 1× in this paper · explained in 3 episodes
Consistency — 1× in this paper · explained in 3 episodes
Contrastive Learning — 1× in this paper · explained in 5 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Differential Privacy (DP) — 1× in this paper · explained in 7 episodes
Distribution Matching — 1× in this paper · explained in 2 episodes
Distribution Shift — 1× in this paper · explained in 5 episodes
Explainability — 1× in this paper · explained in 3 episodes
Federated Learning — 1× in this paper · explained in 14 episodes
Few-Shot Learning — 1× in this paper · explained in 4 episodes
Flow Matching — 1× in this paper · explained in 8 episodes
Generalization — 1× in this paper · explained in 13 episodes
Generative AI — 1× in this paper · explained in 2 episodes
In Network and Distributed System Security Symposium — 1× in this paper
In-Context Learning — 1× in this paper · explained in 4 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Membership Inference Attacks (MIAs) — 1× in this paper · explained in 2 episodes
Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
Prompt Engineering — 1× in this paper · explained in 4 episodes
Pruning — 1× in this paper · explained in 4 episodes
RAG — 1× in this paper · explained in 3 episodes
Regularization — 1× in this paper · explained in 3 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Scaling Laws — 1× in this paper · explained in 4 episodes
Stable Diffusion — 1× in this paper
Steering — 1× in this paper · explained in 2 episodes
Verification — 1× in this paper · explained in 2 episodes

When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training

Terminology

Sources

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