Tackling Fake Forgetting through Uncertainty Quantification

Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy is a widely used metric for assessing unlearning performance, it falls short in assessing the reliability of forgetting. In this paper, we find that the forget data points misclassified by unlearning accuracy still have their ground truth labels included in the conformal prediction set from the uncertainty quantification perspective, leading to a phenomenon we term fake forgetting. To address this issue, we propose a novel metric CR, inspired by conformal prediction, that offers a more reliable assessment of forgetting quality. Building on these insights, we further propose an unlearning framework CPU that incorporates conformal prediction into the Carlini & Wagner adversarial attack loss, enabling the ground truth label to be effectively removed from the conformal prediction set. Through extensive experiments on image classification tasks, we demonstrate both the effectiveness of our proposed metric and the superior forgetting quality achieved by our framework. Code is available at https://github.com/TIML-Group/Conformal-Prediction-Unlearning . visualize the important feature of models' prediction by using Grad-CAM [28], further 37 phenomenon. Despite the Finetune method incorrectly predicting the forget data, the 38 maps still focus heavily on the important features of the object itself. 39 Based on the above findings, we design two novel metrics that capture the uncertainty an 40 of unlearning performance more effectively inspired by conformal prediction. Additionall 41 by Carlini & Wagner (C&W) attack [6] and conformal prediction, we propose a genera 42 framework to improve training-based unlearning methods and promote reliable forgetting. 43 maps of our method in Table 1 reveal that once the true label no longer falls within th 44 prediction set, the activation regions shift significantly. To sum up, the contributions of o 45 as follows: 46 • We identify pivotal limitations in current unlearning metrics, as they overlook m 47 data where ground truth labels remain potential candidates under uncertainty qu 48 • We design two novel metrics to address the limitations motivated by conforma 49 • We propose a general unlearning framework for training-based machine unlearn 50 motivated by conformal prediction and C&W loss. 51 • Extensive experiments demonstrate the effectiveness of novel metrics and ou 52 framework. 53 2 Enhancing Metrics for Machine Unlearning Based on Conformal P 54 2.1 Preliminaries and Notations 55 Machine Unlearning. Machine unlearning is the targeted removal of certain training 56 from a machine learning model. In our work, two different forgetting scenarios are co 57 random data forgetting focuses on randomly forgetting specific instances within the traini 58 (ii) class-wise forgetting aims to remove all information associated with an entire class 59 denote the original training data used to obtain an original model ✓ o . We split the wh 60 data D train into two subsets, forget data D f and retain data D r = D train D f . In r 61 forgetting, D test represents test data. In class-wise forgetting, D tf corresponds to the tes 62 Tackling Fake Forgetting through Uncertainty Quantification forgetting focuses on randomly forgetting specific data instances within the training data, and (ii) class-wise forgetting aims to remove all data information associated with an entire class. We also report the results of the worst-case and subclass-wise forgetting scenarios in Tables 11 and 12 in Appendix G. Let D train denote the original training data used to obtain an original model θ o . We split the whole training data D train into two subsets, forget data D f and retain data D r = D train D f . Let D test represent test data. θ u denotes the model after the unlearning process. Conformal Prediction. Conformal prediction (CP) is proposed to quantify uncertainty, providing prediction sets that contain the ground truth label with a theoretically guaranteed probability (Angelopoulos & Bates, 2021). Among the various types of conformal prediction, this work mainly focuses on split conformal prediction (SCP) 1 since it is the most straightforward and easy-to-implement approach. We also report results of other conformal prediction techniques in Appendix F.1. To construct a conformal prediction set, SCP involves four steps on the unlearning model: