Cover Image

The Measure of Deception: An Analysis of Data Forging in Machine Unlearning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dixit, Rishabh, Hui, Yuan, Saab, Rayan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks
by: Zhang, Shihao, et al.
Published: (2025)

Beacon: Post-Training Quantization with Integrated Grid Selection
by: Zhang, Shihao, et al.
Published: (2025)

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation
by: Zhang, Shihao, et al.
Published: (2026)

Unified Stochastic Framework for Neural Network Quantization and Pruning
by: Zhang, Haoyu, et al.
Published: (2024)

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
by: Naderloui, Nima, et al.
Published: (2025)

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
by: Zhang, Shihao, et al.
Published: (2025)

Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection
by: Beck, Nathan, et al.
Published: (2024)

Complement Submodular Information Measures for Balanced and Robust Data Selection
by: Iyer, Rishabh
Published: (2026)

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos
by: Zhang, Haoyu, et al.
Published: (2025)

Data Augmentation Improves Machine Unlearning
by: Falcao, Andreza M. C., et al.
Published: (2025)

Accumulator-Aware Post-Training Quantization for Large Language Models
by: Colbert, Ian, et al.
Published: (2024)

Benchmarking Federated Machine Unlearning methods for Tabular Data
by: Xiao, Chenguang, et al.
Published: (2025)

Towards Aligned Data Forgetting via Twin Machine Unlearning
by: Niu, Zhenxing, et al.
Published: (2025)

Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
by: Chang, Wenhan, et al.
Published: (2024)

FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
by: Li, Zitong, et al.
Published: (2025)

An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
by: Gao, Yichen, et al.
Published: (2026)

Machine Unlearning for Streaming Forgetting
by: Shen, Shaofei, et al.
Published: (2025)

Sharpness-Aware Machine Unlearning
by: Tang, Haoran, et al.
Published: (2025)

Fairness and Robustness in Machine Unlearning
by: Tran, Khoa, et al.
Published: (2025)

Reference-Guided Machine Unlearning
by: Mirlach, Jonas, et al.
Published: (2026)

A Review on Machine Unlearning
by: Zhang, Haibo, et al.
Published: (2024)

Towards Natural Machine Unlearning
by: He, Zhengbao, et al.
Published: (2024)

TraceHiding: Scalable Machine Unlearning for Mobility Data
by: Faraji, Ali, et al.
Published: (2025)

Distributional Machine Unlearning via Selective Data Removal
by: Allouah, Youssef, et al.
Published: (2025)

SoK: Machine Unlearning for Large Language Models
by: Ren, Jie, et al.
Published: (2025)

Towards Irreversible Machine Unlearning for Diffusion Models
by: Yuan, Xun, et al.
Published: (2025)

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
by: Fang, Cheng, et al.
Published: (2025)

Adversarial Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)

Random Vector Functional Link Networks for Function Approximation on Manifolds
by: Needell, Deanna, et al.
Published: (2020)

Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)

Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
by: Lee, Hong kyu, et al.
Published: (2024)

Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
by: Chien, Eli, et al.
Published: (2024)

On the Impossibility of Retrain Equivalence in Machine Unlearning
by: Yu, Jiatong, et al.
Published: (2025)

Efficient Verified Machine Unlearning For Distillation
by: Quan, Yijun, et al.
Published: (2025)

Towards Source-Free Machine Unlearning
by: Ahmed, Sk Miraj, et al.
Published: (2025)

Verifiable and Provably Secure Machine Unlearning
by: Eisenhofer, Thorsten, et al.
Published: (2022)

Revisiting Machine Unlearning with Dimensional Alignment
by: Seo, Seonguk, et al.
Published: (2024)

Statistical Roughness-Informed Machine Unlearning
by: Partohaghighi, Mohammad, et al.
Published: (2026)

Dataset Condensation Driven Machine Unlearning
by: Khan, Junaid Iqbal
Published: (2024)

Label Smoothing Improves Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)