Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models

• URL: http://arxiv.org/abs/2510.21783v1
• Date: Sat, 18 Oct 2025 16:28:48 GMT
• Title: Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models
• Authors: Guo Li, Yuyang Yu, Xuemiao Xu,
• Abstract summary: A key concern is membership inference attacks, which attempt to determine whether a particular data sample was used in the model training process.<n>We propose an efficient membership inference attack method against diffusion models.<n>Our method can also show better attack effects in ASR and AUC when facing large-scale text-to-image diffusion models.
• Score: 19.763802072516228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have demonstrated powerful performance in generating high-quality images. A typical example is text-to-image generator like Stable Diffusion. However, their widespread use also poses potential privacy risks. A key concern is membership inference attacks, which attempt to determine whether a particular data sample was used in the model training process. We propose an efficient membership inference attack method against diffusion models. This method is based on the injection of slight noise and the evaluation of the aggregation degree of the noise distribution. The intuition is that the noise prediction patterns of diffusion models for training set samples and non-training set samples exhibit distinguishable differences.Specifically, we suppose that member images exhibit higher aggregation of predicted noise around a certain time step of the diffusion process. In contrast, the predicted noises of non-member images exhibit a more discrete characteristic around the certain time step. Compared with other existing methods, our proposed method requires fewer visits to the target diffusion model. We inject slight noise into the image under test and then determine its membership by analyzing the aggregation degree of the noise distribution predicted by the model. Empirical findings indicate that our method achieves superior performance across multiple datasets. At the same time, our method can also show better attack effects in ASR and AUC when facing large-scale text-to-image diffusion models, proving the scalability of our method.

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