Copertina

AMUN: Adversarial Machine UNlearning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ebrahimpour-Boroojeny, Ali, Sundaram, Hari, Chandrasekaran, Varun
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

LOTOS: Layer-wise Orthogonalization for Training Robust Ensembles
di: Ebrahimpour-Boroojeny, Ali, et al.
Pubblicazione: (2024)

Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
di: Wang, Xiangwen, et al.
Pubblicazione: (2026)

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
di: Ebrahimpour-Boroojeny, Ali, et al.
Pubblicazione: (2025)

Spectrum Extraction and Clipping for Implicitly Linear Layers
di: Boroojeny, Ali Ebrahimpour, et al.
Pubblicazione: (2024)

Challenges in Enabling Private Data Valuation
di: Fu, Yiwei, et al.
Pubblicazione: (2026)

Toward Reliable Machine Unlearning: Theory, Algorithms, and Evaluation
di: Ebrahimpour-Boroojeny, Ali
Pubblicazione: (2025)

Bypassing LLM Watermarks with Color-Aware Substitutions
di: Wu, Qilong, et al.
Pubblicazione: (2024)

The Erasure Illusion: Stress-Testing the Generalization of LLM Forgetting Evaluation
di: Jia, Hengrui, et al.
Pubblicazione: (2025)

Privately Aligning Language Models with Reinforcement Learning
di: Wu, Fan, et al.
Pubblicazione: (2023)

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
di: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Pubblicazione: (2024)

Adversarial Attacks on Locally Private Graph Neural Networks
di: Varun, Matta, et al.
Pubblicazione: (2026)

Adversarial Machine Unlearning
di: Di, Zonglin, et al.
Pubblicazione: (2024)

It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
di: Annamalai, Meenatchi Sundaram Muthu Selva
Pubblicazione: (2024)

A Geometric Framework for Adversarial Vulnerability in Machine Learning
di: Bell, Brian
Pubblicazione: (2024)

Generation of Human Comprehensible Access Control Policies from Audit Logs
di: Kumar, Gautam, et al.
Pubblicazione: (2026)

FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats
di: Shapira, Avishag, et al.
Pubblicazione: (2025)

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
di: Lu, Bingguang, et al.
Pubblicazione: (2025)

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
di: Angioni, Daniele, et al.
Pubblicazione: (2024)

TA3: Testing Against Adversarial Attacks on Machine Learning Models
di: Jin, Yuanzhe, et al.
Pubblicazione: (2024)

Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
di: Razavi-Far, Roozbeh, et al.
Pubblicazione: (2026)

Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning
di: Hasan, Syed Mhamudul, et al.
Pubblicazione: (2024)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
di: Feng, Chen, et al.
Pubblicazione: (2024)

Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification
di: Bamdad, Amirmohammad, et al.
Pubblicazione: (2025)

Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective
di: Wu, Baoyuan, et al.
Pubblicazione: (2023)

ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
di: Floris, Giuseppe, et al.
Pubblicazione: (2023)

On the Robustness of Malware Detectors to Adversarial Samples
di: Salman, Muhammad, et al.
Pubblicazione: (2024)

A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data
di: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Pubblicazione: (2023)

The Efficacy of Transfer-based No-box Attacks on Image Watermarking: A Pragmatic Analysis
di: Wu, Qilong, et al.
Pubblicazione: (2024)

A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
di: Tafreshian, Benyamin, et al.
Pubblicazione: (2025)

The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging
di: Ganev, Georgi, et al.
Pubblicazione: (2024)

CECILIA: Comprehensive Secure Machine Learning Framework
di: Ünal, Ali Burak, et al.
Pubblicazione: (2022)

Adversarial Machine Learning Threats to Spacecraft
di: Thummala, Rajiv, et al.
Pubblicazione: (2024)

Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
di: Askarizadeh, Mohammad Javad, et al.
Pubblicazione: (2024)

Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
di: Ali, Hassan, et al.
Pubblicazione: (2023)

Detecting Adversarial Data via Provable Adversarial Noise Amplification
di: Mumcu, Furkan, et al.
Pubblicazione: (2026)

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
di: Jin, Zhibo, et al.
Pubblicazione: (2024)

Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators
di: Schulze, J. -P., et al.
Pubblicazione: (2021)

Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
di: Hamidi, Shayan Mohajer, et al.
Pubblicazione: (2024)

Generalization Properties of Adversarial Training for $\ell_0$-Bounded Adversarial Attacks
di: Delgosha, Payam, et al.
Pubblicazione: (2024)

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
di: Ganev, Georgi, et al.
Pubblicazione: (2025)