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

arXiv:2510.21783v2 Announce Type: replace-cross 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. Existing membership inference attacks against diffusion models either directly exploit sample loss differences or rely on image-level reconstruction differences. Both approaches commonly ignore the consistency characteristics of noise prediction during the diffusion process, resulting in either low inference accuracy or high computational costs. To address these shortcomings, we propose a membership inference method based on noise aggregation analysis, and introduce a single-step, low-intensity noise injection diffusion strategy to amplify differences between member and non-member samples. Our proposed approach substantially reduces model query requirements while delivering more efficient and accurate membership inference.

Related

• CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder
• Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation
• ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation
• DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation
• From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity
• Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning

Source: arXiv cs.AI | 2026-04-20