Amplifying Membership Signal Through Chained Regeneration

• URL: http://arxiv.org/abs/2606.31991v1
• Date: Tue, 30 Jun 2026 17:29:04 GMT
• Title: Amplifying Membership Signal Through Chained Regeneration
• Abstract summary: Current membership and dataset inference attacks often rely on one-shot generations.<n>We introduce MADreMIA, a model-agnostic framework that enhances white-, gray-, and black-box MIA and DI.<n>Our results show that MADreMIA provides richer signals across diverse model families and modalities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The tendency of large generative models to memorize training data makes sample verification critical for privacy auditing and copyright enforcement. Current membership (MIA) and dataset inference (DI) attacks often rely on one-shot generations, which yield weak signals and limited sensitivity across modalities. Inspired by Model Autophagy Disorder (MAD), we introduce MADreMIA, a model-agnostic framework that enhances white-, gray-, and black-box MIA and DI. Rather than relying on shadow model training -- often infeasible for large generative models -- our framework facilitates scalable inference by leveraging inherent signals through iterative trajectories. This process utilizes chained generations across diverse modalities, where each output serves as the subsequent input, to improve membership evidence at low FPR. We demonstrate that memorized training samples exhibit significantly higher coherence and slower degradation during iterative regeneration than non-member generations. Our results show that MADreMIA provides richer signals across diverse model families and modalities; we present comprehensive evaluations for IARs, diffusion, and language models, alongside preliminary results demonstrating its potential for audio models.

Related papers

• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• Improving Text Style Transfer using Masked Diffusion Language Models with Inference-time Scaling [37.795834398730555]
  Masked diffusion language models (MDMs) have recently gained traction as a viable generative framework for natural language.<n>We propose a verifier-based inference-time scaling method that aids in finding a better candidate generation during the denoising process of the MDM.<n>Our experiments demonstrate the application of MDMs for standard text-style transfer tasks and establish MDMs as a better alternative to autoregressive language models.
  arXiv  Detail & Related papers  (2025-08-14T18:01:22Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models generate target documents directly from a query.<n>We systematically investigate training and inference scaling laws in generative retrieval, exploring how model size, training data scale, and inference-time compute jointly influence performance.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models.<n>For the first time, that order of generation can successfully be predicted by the denoising network itself.<n>Using these findings, we can increase the accuracy of specific reasoning tasks from 1% to >50%.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias [47.79659355705916]
  Model-induced distribution shifts (MIDS) occur as previous model outputs pollute new model training sets over generations of models. We introduce a framework that allows us to track multiple MIDS over many generations, finding that they can lead to loss in performance, fairness, and minoritized group representation. Despite these negative consequences, we identify how models might be used for positive, intentional, interventions in their data ecosystems.
  arXiv  Detail & Related papers  (2024-03-12T17:48:08Z)
• A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models [31.06882321425704]
  Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model.<n>We propose a Probabilistic Fluctuation Assessing Membership Inference Attack (PFAMI)<n>PFAMI can improve the attack success rate (ASR) by about 27.9% when compared with the best baseline.
  arXiv  Detail & Related papers  (2023-08-23T14:00:58Z)
• Restoration based Generative Models [0.886014926770622]
  Denoising diffusion models (DDMs) have attracted increasing attention by showing impressive synthesis quality. In this paper, we establish the interpretation of DDMs in terms of image restoration (IR) We propose a multi-scale training, which improves the performance compared to the diffusion process, by taking advantage of the flexibility of the forward process. We believe that our framework paves the way for designing a new type of flexible general generative model.
  arXiv  Detail & Related papers  (2023-02-20T00:53:33Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.