CURE: Centroid-guided Unsupervised Representation Erasure for Facial Recognition Systems

• URL: http://arxiv.org/abs/2509.19562v1
• Date: Tue, 23 Sep 2025 20:42:40 GMT
• Title: CURE: Centroid-guided Unsupervised Representation Erasure for Facial Recognition Systems
• Authors: Fnu Shivam, Nima Najafzadeh, Yenumula Reddy, Prashnna Gyawali,
• Abstract summary: We introduce CURE, the first unsupervised unlearning framework for facial recognition systems.<n>CURE effectively removes targeted samples while preserving overall performance.<n>We also propose a novel metric, the Unlearning Efficiency Score (UES), which balances forgetting and retention stability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the current digital era, facial recognition systems offer significant utility and have been widely integrated into modern technological infrastructures; however, their widespread use has also raised serious privacy concerns, prompting regulations that mandate data removal upon request. Machine unlearning has emerged as a powerful solution to address this issue by selectively removing the influence of specific user data from trained models while preserving overall model performance. However, existing machine unlearning techniques largely depend on supervised techniques requiring identity labels, which are often unavailable in privacy-constrained situations or in large-scale, noisy datasets. To address this critical gap, we introduce CURE (Centroid-guided Unsupervised Representation Erasure), the first unsupervised unlearning framework for facial recognition systems that operates without the use of identity labels, effectively removing targeted samples while preserving overall performance. We also propose a novel metric, the Unlearning Efficiency Score (UES), which balances forgetting and retention stability, addressing shortcomings in the current evaluation metrics. CURE significantly outperforms unsupervised variants of existing unlearning methods. Additionally, we conducted quality-aware unlearning by designating low-quality images as the forget set, demonstrating its usability and benefits, and highlighting the role of image quality in machine unlearning.

Related papers

• Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs [10.135445130232265]
  We propose Shadow Unlearning, a novel paradigm of approximate unlearning, that performs machine unlearning on anonymized forget data without exposing PII.<n>We further propose a novel privacy-preserving framework, Neuro-Semantic Projector Unlearning (NSPU) to achieve Shadow unlearning.<n> Experimental results show that NSPU achieves superior unlearning performance, preserves model utility, and enhances user privacy.
  arXiv  Detail & Related papers  (2026-01-07T12:11:25Z)
• Face Identity Unlearning for Retrieval via Embedding Dispersion [0.0]
  Face recognition systems rely on learning highly discriminative and compact identity clusters to enable accurate retrieval.<n>In this work, we study the problem of face identity unlearning for retrieval systems and present its inherent challenges.<n>The primary challenge is to achieve this forgetting effect while preserving the discriminative structure of the embedding space.
  arXiv  Detail & Related papers  (2025-12-15T13:35:28Z)
• A Deep Learning Approach for Facial Attribute Manipulation and Reconstruction in Surveillance and Reconnaissance [5.980822697955566]
  Surveillance systems play a critical role in security and reconnaissance, but their performance is often compromised by low-quality images and videos.<n>Existing AI-based facial analysis models suffer from biases related to skin tone variations and partially occluded faces.<n>We propose a data-driven platform that enhances surveillance capabilities by generating synthetic training data tailored to compensate for dataset biases.
  arXiv  Detail & Related papers  (2025-06-06T23:09:17Z)
• Does Machine Unlearning Truly Remove Knowledge? [80.83986295685128]
  We introduce a comprehensive auditing framework for unlearning evaluation comprising three benchmark datasets, six unlearning algorithms, and five prompt-based auditing methods.<n>We evaluate the effectiveness and robustness of different unlearning strategies.
  arXiv  Detail & Related papers  (2025-05-29T09:19:07Z)
• A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning [67.72413262980272]
  Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear.<n>We develop SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck.<n>Our approach achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations.
  arXiv  Detail & Related papers  (2025-03-10T06:18:31Z)
• Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset [92.99416966226724]
  We introduce Facial Identity Unlearning Benchmark (FIUBench), a novel VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms.<n>We apply a two-stage evaluation pipeline that is designed to precisely control the sources of information and their exposure levels.<n>Through the evaluation of four baseline VLM unlearning algorithms within FIUBench, we find that all methods remain limited in their unlearning performance.
  arXiv  Detail & Related papers  (2024-11-05T23:26:10Z)
• Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning [7.557226714828334]
  We present a novel unlearning mechanism designed to remove the impact of specific data samples from a neural network. In achieving this goal, we crafted a novel loss function tailored to eliminate privacy-sensitive information from weights and activation values of the target model. Our results showcase the superior performance of our approach in terms of unlearning efficacy and latency as well as the fidelity of the primary task.
  arXiv  Detail & Related papers  (2024-07-01T00:20:26Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness [30.596695293390415]
  We introduce the task of Lifecycle Unlearning Commitment Management (LUCM) for approximate unlearning. We propose an efficient metric designed to assess the sample-level unlearning completeness. We show that this metric is able to serve as a tool for monitoring unlearning anomalies throughout the unlearning lifecycle.
  arXiv  Detail & Related papers  (2024-03-19T15:37:27Z)
• Towards Independence Criterion in Machine Unlearning of Features and Labels [9.790684060172662]
  This work delves into the complexities of machine unlearning in the face of distributional shifts. Our research introduces a novel approach that leverages influence functions and principles of distributional independence to address these challenges. Our method not only facilitates efficient data removal but also dynamically adjusts the model to preserve its generalization capabilities.
  arXiv  Detail & Related papers  (2024-03-12T23:21:09Z)
• Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings [22.338328674283062]
  We introduce an innovative image transformation technique that renders facial images unrecognizable to the eye while maintaining their identifiability by neural network models. The proposed methodology can be used in various artificial intelligence applications to distort the visual data and keep the derived features close. We show that it is possible to build the distortion that changes the image content by more than 70% while maintaining the same recognition accuracy.
  arXiv  Detail & Related papers  (2024-01-26T18:20:53Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Suppressing Uncertainties for Large-Scale Facial Expression Recognition [81.51495681011404]
  This paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with textbf88.14% on RAF-DB, textbf60.23% on AffectNet, and textbf89.35% on FERPlus.
  arXiv  Detail & Related papers  (2020-02-24T17:24:36Z)

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.