Adversarial Representation Learning

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Transferable Adversarial Robustness for Speech Foundation Models via Hierarchical Stabilization
Oct 4, 2026Aref Mousavi, Shahab Sherafat, Kiarash Kiani Feriz +4Adversarial Representation Learning Speech Foundation Models
Sharif University of Technology · University of Tehran · New Uzbekistan University +1
Adversarial Consistency-Guided Representation Learning for Multi-view Clustering
Sep 28, 2026Yuchen Lin, Kunpeng Xu, Ying Fang +1Multi-View Consistency Adversarial Representation Learning
School of Computer and Cyber Security, Fujian Normal University, Fuzhou 350000, China · Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou 350000, China · School of Computer Science, McGill University, Montreal H3A 2A7, Quebec, Canada
Damnatio Memoriae: Adversarially and Selectively Forgetting Identities in the Embedding Space of Face Recognition Models
Sep 22, 2026Ünsal Öztürk, Vedrana Krivokuća Hahn, Sushil Bhattacharjee +1Adversarial Representation Learning Face Recognition
Idiap Research Institute, Martigny, Switzerland · UNIL, Lausanne, Switzerland
NeuroPriv: Adversarial Representation Learning for Privacy in Wearable EEG Systems
Aug 31, 2026Sarmistha Sarna Gomasta, Bhawana Chhaglani, Prashant ShenoyAdversarial Training Electroencephalography
University of Massachusetts Amherst · United States
The Devil Is in the Leakage: A Disentangled Dual-Purification Framework for High-Fidelity Hairstyle Transfer
Jul 13, 2026Jijie Li, Jiankuo Zhao, Xiangyu Zhu +1Adversarial Representation Learning Image Style Transfer
SAI, UCAS; MAIS, CASIA Beijing, China · CAIR, HKSIS, CAS Hong Kong, China · SCSE, FIE, M.U.S.T Macau, China
Conservative Subject Invariant EMG-based Gesture Recognition
Jul 4, 2026Hamed Rafiei, Ali MousaviEMG-Based Gesture Recognition Adversarial Representation Learning
Department of Electrical Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran · Department of Computer Engineering, Ne. C., Islamic Azad University, Neyshabur, Iran
Fair Cognitive Impairment Detection Through Unlearning
Jun 17, 2026William Nguyen, Jiali Cheng, Hadi AmiriAdversarial Representation Learning Algorithmic Fairness
University of Massachusetts Lowell, USA
GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
Jun 8, 2026Dasari Naga RajuAdversarial Representation Learning Cancer Survival Prediction
TabChange: Precise Attribute Changes in Tabular Data
May 30, 2026Arjun Dahal, Yu Lei, Raghu N. Kacker +1Adversarial Representation Learning Counterfactual Explanations
The University of Texas at Arlington · Arlington, Texas, USA · Information Technology Laboratory +2
Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation
May 25, 2026Ruoxi Cheng, Haoxuan Ma, Zhengfei Hai +6Adversarial Representation Learning Vision-Language Models
Fudan University · Tencent · Nanjing University +3
LCGuard: Latent Communication Guard for Safe KV Sharing in Multi-Agent Systems
May 21, 2026Sadia Asif, Mohammad Mohammadi Amiri, Momin Abbas +2Adversarial Representation Learning Privacy-Preserving Language Models
Rensselaer Polytechnic Institute · IBM Research
LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation
May 1, 2026Venkata Pushpak Teja MentaAdversarial Training Adversarial Representation Learning
Praxel Ventures

Oct 4, 2026, eess.ASJ/K move · Enter open · S save

Transferable Adversarial Robustness for Speech Foundation Models via Hierarchical Stabilization

Aref Mousavi, Shahab Sherafat, Kiarash Kiani Feriz, Amirparsa Safari+3

Sharif University of Technology · University of Tehran · New Uzbekistan University+1

Adversarial Representation Learning Speech Foundation Models Adversarial Robustness

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Frozen speech foundation models (SFMs) make downstream adaptation efficient: the backbone can stay fixed while a task learns layer fusion and a lightweight classifier. Full adversarial fine-tuning is a standard route to robustness, but generating adversarial examples and updating the backbone for every task sacrifices that efficiency. We ask whether robustness can instead be learned before future tasks are known. For a frozen backbone and linear classifier, robustness can be understood through the interaction between representation stability and decision-boundary margin. This leads directly to our design: we stabilize representations across the hidden layers, rather than only the final layer, while preserving clean representations; after clean adaptation selects the layer mixture, we keep it fixed and enlarge only the classifier margin, without downstream adversarial examples. We evaluate Wav2Vec2, HuBERT, and WavLM Large on four tasks under adaptive 30 dB attacks. Across 12 backbone-task pairs, hierarchical robustification improves robust accuracy by 46.4 pp, while margin refinement adds 4.0 pp for 1.1 pp of clean accuracy. Code and configurations are available at https://github.com/arefmousavi/hierarchical-robust-sfm.

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