Cover Image

Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chang, Jung-Woo, Sun, Ke, Heydaribeni, Nasimeh, Hidano, Seira, Zhang, Xinyu, Koushanfar, Farinaz
Format:	Preprint
Published:	2023
Subjects:	Cryptography and Security Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

EveGuard: Defeating Vibration-based Side-Channel Eavesdropping with Audio Adversarial Perturbations
by: Chang, Jung-Woo, et al.
Published: (2024)

Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
by: Hidano, Seira, et al.
Published: (2022)

Props for Machine-Learning Security
by: Juels, Ari, et al.
Published: (2024)

MergeGuard: Efficient Thwarting of Trojan Attacks in Machine Learning Models
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2025)

AI Security Map: Holistic Organization of AI Security Technologies and Impacts on Stakeholders
by: Kato, Hiroya, et al.
Published: (2025)

EmMark: Robust Watermarks for IP Protection of Embedded Quantized Large Language Models
by: Zhang, Ruisi, et al.
Published: (2024)

Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
by: Zhang, Ruisi, et al.
Published: (2024)

ICtoken: An NFT for Hardware IP Protection
by: Balla, Shashank, et al.
Published: (2024)

PAC to the Future: Zero-Knowledge Proofs of PAC Private Systems
by: Repetto, Guilhem, et al.
Published: (2026)

Zero-Knowledge Proof Frameworks: A Systematic Survey
by: Sheybani, Nojan, et al.
Published: (2025)

SWaRL: Safeguard Code Watermarking via Reinforcement Learning
by: Javidnia, Neusha, et al.
Published: (2026)

CryptoGen: Secure Transformer Generation with Encrypted KV-Cache Reuse
by: Zhang, Hedong, et al.
Published: (2026)

REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models
by: Zhang, Ruisi, et al.
Published: (2023)

Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
by: Jandali, Yaman, et al.
Published: (2025)

Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign
by: Zhang, Ruisi, et al.
Published: (2025)

Automated Physical Design Watermarking Leveraging Graph Neural Networks
by: Zhang, Ruisi, et al.
Published: (2024)

Trojan Cleansing with Neural Collapse
by: Gu, Xihe, et al.
Published: (2024)

ICMarks: A Robust Watermarking Framework for Integrated Circuit Physical Design IP Protection
by: Zhang, Ruisi, et al.
Published: (2024)

AttestLLM: Efficient Attestation Framework for Billion-scale On-device LLMs
by: Zhang, Ruisi, et al.
Published: (2025)

LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning
by: Spracklen, Joseph, et al.
Published: (2026)

A Comprehensive Review of Adversarial Attacks on Machine Learning
by: Ahmed, Syed Quiser, et al.
Published: (2024)

ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
by: Sheybani, Nojan, et al.
Published: (2025)

CAPAA: Classifier-Agnostic Projector-Based Adversarial Attack
by: Li, Zhan, et al.
Published: (2025)

Gotta Hash 'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications
by: Sheybani, Nojan, et al.
Published: (2025)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
by: Xue, Jing, et al.
Published: (2025)

DROP: Poison Dilution via Knowledge Distillation for Federated Learning
by: Syros, Georgios, et al.
Published: (2025)

Adversarial Attack Based Countermeasures against Deep Learning Side-Channel Attacks
by: Gu, Ruizhe, et al.
Published: (2020)

SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection
by: Heydaribeni, Nasimeh, et al.
Published: (2023)

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

AMAZE: Accelerated MiMC Hardware Architecture for Zero-Knowledge Applications on the Edge
by: Ahmed, Anees, et al.
Published: (2024)

Adversarial Machine Learning: Attacks, Defenses, and Open Challenges
by: Jha, Pranav K
Published: (2025)

The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries
by: Lu, Tianpei, et al.
Published: (2024)

Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models
by: Huo, Mingjia, et al.
Published: (2024)

Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices
by: Elsayed, Nelly, et al.
Published: (2025)

"Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval
by: Li, Jiate, et al.
Published: (2026)

Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors
by: Zhang, Hangsheng, et al.
Published: (2024)

Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification
by: Ma, Jie, et al.
Published: (2025)

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
by: Chen, Jing, et al.
Published: (2025)

Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs
by: Yang, Yuxiang, et al.
Published: (2026)

A Survey of Machine Learning-based Physical-Layer Authentication in Wireless Communications
by: Meng, Rui, et al.
Published: (2024)