Title: Taming Latent Diffusion Model for Black-Box Model Inversion Attacks
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Model Inversion Attacks, Latent Diffusion Models, Adapter Network

Abstract: Model inversion attacks (MIAs) seriously threaten privacy by querying models to generate synthetic images that expose features of private training data. These attacks target deep neural networks used in sensitive fields like user authentication. Previous MIA methods based on generative adversarial networks have been proven to be effective, and there are preliminary studies exploring the potential of diffusion models in this field. In this work, we further investigated the feasibility of applying large-scale text to-image latent diffusion models (LDMs) to MIA. To overcome challenges of high training costs from scratch and the complexity of conditional encoding, we propose a novel method that fine-tunes an LDM via an adapter network. Our MI-ControlNet framework integrates probability distributions into the attention module to guide latent space generation. We also train a surrogate model through knowledge transfer and integrated identity loss into the training process of MI-ControlNet, thus establishing a new training paradigm. Experiments on various models and datasets have shown that our method achieves excellent performance in black-box MIAs. Code will be made available following the review process.

TL;DR: MI-ControlNet: A novel model inversion attack framework by fine-tuning latent diffusion models (LDMs), integrating probability-guided attention and surrogate-based identity loss to achieve superior black-box performance without full LDM retraining.