How Far Are We from True Unlearnability?

• URL: http://arxiv.org/abs/2509.08058v1
• Date: Tue, 09 Sep 2025 18:01:10 GMT
• Title: How Far Are We from True Unlearnability?
• Authors: Kai Ye, Liangcai Su, Chenxiong Qian,
• Abstract summary: Several unlearnable methods have been proposed, which generate unlearnable examples (UEs) by compromising the training availability of data.<n>We investigate how far are we from attaining truly unlearnable examples?<n>We propose an Unlearnable Distance (UD) to measure the unlearnability of data based on the SAL distribution of parameters in clean and poisoned models.
• Score: 8.176905459241047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality data plays an indispensable role in the era of large models, but the use of unauthorized data for model training greatly damages the interests of data owners. To overcome this threat, several unlearnable methods have been proposed, which generate unlearnable examples (UEs) by compromising the training availability of data. Clearly, due to unknown training purposes and the powerful representation learning capabilities of existing models, these data are expected to be unlearnable for models across multiple tasks, i.e., they will not help improve the model's performance. However, unexpectedly, we find that on the multi-task dataset Taskonomy, UEs still perform well in tasks such as semantic segmentation, failing to exhibit cross-task unlearnability. This phenomenon leads us to question: How far are we from attaining truly unlearnable examples? We attempt to answer this question from the perspective of model optimization. To this end, we observe the difference in the convergence process between clean and poisoned models using a simple model architecture. Subsequently, from the loss landscape we find that only a part of the critical parameter optimization paths show significant differences, implying a close relationship between the loss landscape and unlearnability. Consequently, we employ the loss landscape to explain the underlying reasons for UEs and propose Sharpness-Aware Learnability (SAL) to quantify the unlearnability of parameters based on this explanation. Furthermore, we propose an Unlearnable Distance (UD) to measure the unlearnability of data based on the SAL distribution of parameters in clean and poisoned models. Finally, we conduct benchmark tests on mainstream unlearnable methods using the proposed UD, aiming to promote community awareness of the capability boundaries of existing unlearnable methods.

Related papers

• REMIND: Input Loss Landscapes Reveal Residual Memorization in Post-Unlearning LLMs [0.1784233255402269]
  Machine unlearning aims to remove the influence of specific training data from a model without requiring full retraining.<n>We propose REMIND, a novel evaluation method aiming to detect the subtle remaining influence of unlearned data.<n>We show that unlearned data yield flatter, less steep loss landscapes, while retained or unrelated data exhibit sharper, more volatile patterns.
  arXiv  Detail & Related papers  (2025-11-06T09:58:19Z)
• How Does Overparameterization Affect Machine Unlearning of Deep Neural Networks? [1.573034584191491]
  We show how unlearning of deep neural networks (DNNs) is affected by the model parameterization level.<n>We define validation-based tuning for several unlearning methods from the recent literature.
  arXiv  Detail & Related papers  (2025-03-11T17:21:26Z)
• Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA [15.542668474378633]
  We propose a novel and efficient machine unlearning method on pre-trained models. We leverage LoRA to decompose the model's intermediate features into pre-trained features and residual features. The method aims to learn the zero residuals on the retained set and shifted residuals on the unlearning set.
  arXiv  Detail & Related papers  (2024-11-13T08:56:35Z)
• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective [4.31734012105466]
  Machine Unlearning is the process of selectively discarding information designated to specific sets or classes of data from a pre-trained model. We propose a methodology tailored for the purposeful elimination of information linked to a specific class of data from a pre-trained classification network. Our novel approach, termed textbfPartially-Blinded Unlearning (PBU), surpasses existing state-of-the-art class unlearning methods, demonstrating superior effectiveness.
  arXiv  Detail & Related papers  (2024-03-24T17:33:22Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Learning Defect Prediction from Unrealistic Data [57.53586547895278]
  Pretrained models of code have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensurate volumes of training data. It has become popular to train models with far larger but less realistic datasets, such as functions with artificially injected bugs. Models trained on such data tend to only perform well on similar data, while underperforming on real world programs.
  arXiv  Detail & Related papers  (2023-11-02T01:51:43Z)
• Non-Invasive Fairness in Learning through the Lens of Data Drift [88.37640805363317]
  We show how to improve the fairness of Machine Learning models without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data.
  arXiv  Detail & Related papers  (2023-03-30T17:30:42Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Uncertainty Estimation for Language Reward Models [5.33024001730262]
  Language models can learn a range of capabilities from unsupervised training on text corpora. It is often easier for humans to choose between options than to provide labeled data, and prior work has achieved state-of-the-art performance by training a reward model from such preference comparisons. We seek to address these problems via uncertainty estimation, which can improve sample efficiency and robustness using active learning and risk-averse reinforcement learning.
  arXiv  Detail & Related papers  (2022-03-14T20:13:21Z)
• Machine Unlearning of Features and Labels [72.81914952849334]
  We propose first scenarios for unlearning and labels in machine learning models. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
  arXiv  Detail & Related papers  (2021-08-26T04:42:24Z)

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.