Model Inversion Attacks Through Target-Specific Conditional Diffusion Models

• URL: http://arxiv.org/abs/2407.11424v2
• Date: Thu, 21 Nov 2024 12:22:10 GMT
• Title: Model Inversion Attacks Through Target-Specific Conditional Diffusion Models
• Authors: Ouxiang Li, Yanbin Hao, Zhicai Wang, Bin Zhu, Shuo Wang, Zaixi Zhang, Fuli Feng,
• Abstract summary: Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
• Score: 54.69008212790426
• License:
• Abstract: Model inversion attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. To alleviate these issues, leveraging on diffusion models' remarkable synthesis capabilities, we propose Diffusion-based Model Inversion (Diff-MI) attacks. Specifically, we introduce a novel target-specific conditional diffusion model (CDM) to purposely approximate target classifier's private distribution and achieve superior accuracy-fidelity balance. Our method involves a two-step learning paradigm. Step-1 incorporates the target classifier into the entire CDM learning under a pretrain-then-finetune fashion, with creating pseudo-labels as model conditions in pretraining and adjusting specified layers with image predictions in fine-tuning. Step-2 presents an iterative image reconstruction method, further enhancing the attack performance through a combination of diffusion priors and target knowledge. Additionally, we propose an improved max-margin loss that replaces the hard max with top-k maxes, fully leveraging feature information and soft labels from the target classifier. Extensive experiments demonstrate that Diff-MI significantly improves generative fidelity with an average decrease of 20\% in FID while maintaining competitive attack accuracy compared to state-of-the-art methods across various datasets and models. Our code is available at: \url{https://github.com/Ouxiang-Li/Diff-MI}.

Related papers

• Mitigating Embedding Collapse in Diffusion Models for Categorical Data [52.90687881724333]
  We introduce CATDM, a continuous diffusion framework within the embedding space that stabilizes training. Experiments on benchmarks show that CATDM mitigates embedding collapse, yielding superior results on FFHQ, LSUN Churches, and LSUN Bedrooms.
  arXiv  Detail & Related papers  (2024-10-18T09:12:33Z)
• 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)
• A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks [43.98557963966335]
  Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from released models by utilizing output information. Recent advances in generative adversarial networks (GANs) have contributed significantly to the improved performance of MI attacks. We propose a novel method, Intermediate Features enhanced Generative Model Inversion (IF-GMI), which disassembles the GAN structure and exploits features between intermediate blocks.
  arXiv  Detail & Related papers  (2024-07-18T19:16:22Z)
• Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment [24.049615035939237]
  Model inversion (MI) attack reconstructs the private training data of a target model given its output. We propose a novel Prediction-to-ImageP2I method for black-box MI attack. Our method improves attack accuracy by 8.5% and reduces query numbers by 99% on dataset CelebA.
  arXiv  Detail & Related papers  (2024-07-11T01:58:35Z)
• Gradient Inversion of Federated Diffusion Models [4.1355611383748005]
  Diffusion models are becoming defector generative models, which generate exceptionally high-resolution image data. In this paper, we study the privacy risk of gradient inversion attacks. We propose a triple-optimization GIDM+ that coordinates the optimization of the unknown data.
  arXiv  Detail & Related papers  (2024-05-30T18:00:03Z)
• Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model [14.834360664780709]
  Model attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models. This paper develops a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.
  arXiv  Detail & Related papers  (2023-07-17T12:14:24Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks [13.374754708543449]
  Model attacks (MIAs) aim to create synthetic images that reflect the class-wise characteristics from a target inversion's training data by exploiting the model's learned knowledge. Previous research has developed generative MIAs using generative adversarial networks (GANs) as image priors tailored to a specific target model. We present Plug & Play Attacks that loosen the dependency between the target model and image prior and enable the use of a single trained GAN to attack a broad range of targets.
  arXiv  Detail & Related papers  (2022-01-28T15:25:50Z)
• Knowledge-Enriched Distributional Model Inversion Attacks [49.43828150561947]
  Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data. Our experiments show that the combination of these techniques can significantly boost the success rate of the state-of-the-art MI attacks by 150%.
  arXiv  Detail & Related papers  (2020-10-08T16:20:48Z)

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.