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The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models

Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie

TL;DR

Avatar challenges the illusion of unexploitable data by using diffusion-model denoising to defuse data-protecting perturbations, with a contraction-based theory linking required denoising to perturbation magnitude: $\mathbb{E}[\|\bar{x}_0-x\|^2] \le 2(\mu+1)\Delta$. It demonstrates SOTA performance against seven availability attacks across CIFAR-10/100, SVHN, and ImageNet-100, often outperforming adversarial training, and shows resilience under distribution mismatch. The work provides a practical, data-preprocessing-based defense and an open-source implementation, highlighting the fragility of unexploitable-data claims and prompting further research into protecting personal data across modalities.

Abstract

Protecting personal data against exploitation of machine learning models is crucial. Recently, availability attacks have shown great promise to provide an extra layer of protection against the unauthorized use of data to train neural networks. These methods aim to add imperceptible noise to clean data so that the neural networks cannot extract meaningful patterns from the protected data, claiming that they can make personal data "unexploitable." This paper provides a strong countermeasure against such approaches, showing that unexploitable data might only be an illusion. In particular, we leverage the power of diffusion models and show that a carefully designed denoising process can counteract the effectiveness of the data-protecting perturbations. We rigorously analyze our algorithm, and theoretically prove that the amount of required denoising is directly related to the magnitude of the data-protecting perturbations. Our approach, called AVATAR, delivers state-of-the-art performance against a suite of recent availability attacks in various scenarios, outperforming adversarial training even under distribution mismatch between the diffusion model and the protected data. Our findings call for more research into making personal data unexploitable, showing that this goal is far from over. Our implementation is available at this repository: https://github.com/hmdolatabadi/AVATAR.

The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models

TL;DR

Avatar challenges the illusion of unexploitable data by using diffusion-model denoising to defuse data-protecting perturbations, with a contraction-based theory linking required denoising to perturbation magnitude: . It demonstrates SOTA performance against seven availability attacks across CIFAR-10/100, SVHN, and ImageNet-100, often outperforming adversarial training, and shows resilience under distribution mismatch. The work provides a practical, data-preprocessing-based defense and an open-source implementation, highlighting the fragility of unexploitable-data claims and prompting further research into protecting personal data across modalities.

Abstract

Protecting personal data against exploitation of machine learning models is crucial. Recently, availability attacks have shown great promise to provide an extra layer of protection against the unauthorized use of data to train neural networks. These methods aim to add imperceptible noise to clean data so that the neural networks cannot extract meaningful patterns from the protected data, claiming that they can make personal data "unexploitable." This paper provides a strong countermeasure against such approaches, showing that unexploitable data might only be an illusion. In particular, we leverage the power of diffusion models and show that a carefully designed denoising process can counteract the effectiveness of the data-protecting perturbations. We rigorously analyze our algorithm, and theoretically prove that the amount of required denoising is directly related to the magnitude of the data-protecting perturbations. Our approach, called AVATAR, delivers state-of-the-art performance against a suite of recent availability attacks in various scenarios, outperforming adversarial training even under distribution mismatch between the diffusion model and the protected data. Our findings call for more research into making personal data unexploitable, showing that this goal is far from over. Our implementation is available at this repository: https://github.com/hmdolatabadi/AVATAR.
Paper Structure (34 sections, 5 theorems, 33 equations, 16 figures, 11 tables, 1 algorithm)

This paper contains 34 sections, 5 theorems, 33 equations, 16 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Poisoning and Backdoor Attacks
  4. Availability Attacks
  5. Diffusion Models
  6. Proposed Method
  7. Problem Statement
  8. dAta aVailAbiliTy Attacks defuseR (Avatar)
  9. Conflicting Assumptions in Availability Attacks
  10. Avatar vs. Adversarial Training
  11. Experimental Results
  12. Details of Experimental Settings
  13. Datasets
  14. Classifiers
  15. Diffusion Models
  16. ...and 19 more sections

Key Result

Theorem 1

Let $\boldsymbol{x} \in \mathbb{R}^{d}$ denote a clean image and ${\tilde{\boldsymbol{x}} = \boldsymbol{x} + \boldsymbol{\delta}}$ its protected version, where ${\boldsymbol{\delta}}$ denotes any arbitrary data protection perturbation. Also, let $\bar{\boldsymbol{x}}_{0}$ be the sanitized image usin then the estimation error between the sanitized $\bar{\boldsymbol{x}}_{0}$ and clean image $\boldsy

Figures (16)

  • Figure 1: The threat model considered in this paper. Availability attacks cannot guarantee to protect all the data that exists over the web. A data exploiter might use large density estimators to defuse the data-protecting perturbations and exploit the data.
  • Figure 2: Overview of Avatar. According to a pre-trained diffusion model, we first add a controlled amount of Gaussian noise to the training data. Then, we use the reverse diffusion process to denoise the data which is later going to be used for neural network training.
  • Figure 3: Test accuracy of CIFAR-10, SVHN, and CIFAR-100 classifiers against availability attacks using adversarial training with different perturbation radii.
  • Figure 4: Effect of changing the forward process diffusion timestep in Avatar on the final test accuracy in CIFAR-10 classifiers.
  • Figure 5: Relative error rate of RN-18 models trained against availability attacks on CIFAR-10 and SVHN averaged over 5 runs. Overlapping indicates that the diffusion model and availability attacks use the same subset as training data. Non-overlapping means that the diffusion model and availability attacks are trained on disjoint subsets of data.
  • ...and 11 more figures

Theorems & Definitions (10)

  • Theorem 1
  • proof
  • Theorem 2: Discrete stochastic contraction pham2008analysischung2022come
  • Corollary 2.1
  • proof
  • Lemma 1: chung2022come
  • proof
  • Lemma 2
  • proof
  • proof