mlsanityData to 7 Oct 2026

Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification

arXiv 2512.12002 ↗First posted 12 Dec 2025cs.CR cs.LGcited by 2

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Abstract (arXiv)

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effective adversarial attacks against different types of RFFI classifiers have not yet been explored. In this paper, we carried out a comprehensive investigations into different adversarial attack methods on RFFI systems using various deep learning models. Three specific algorithms, fast gradient sign method (FGSM), projected gradient descent (PGD), and universal adversarial perturbation (UAP), were analyzed. The attacks were launched to LoRa-RFFI and the experimental results showed the generated perturbations were effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU). We further used UAP to launch practical attacks. Special factors were considered for the wireless context, including implementing real-time attacks, the effectiveness of the attacks over a period of time, etc. Our experimental evaluation demonstrated that UAP can successfully launch adversarial attacks against the RFFI, achieving a success rate of 81.7% when the adversary almost has no prior knowledge of the victim RFFI systems.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 56 references that are in the map. First posted on arXiv 12 Dec 2025.

Builds on

25 of 56 references.

References · 25

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  4. 2016Universal adversarial perturbationscited by 774
  5. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  6. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 123
  7. 2018Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attackscited by 46
  8. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  9. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  10. 2020Adversarial Machine Learning in Wireless Communications using RF Data: A Reviewcited by 25
  11. 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
  12. 2019Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systemscited by 19
13 more
  1. 2019The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detectioncited by 17
  2. 2020Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerationscited by 16
  3. 2021Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRacited by 16
  4. 2019Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domaincited by 14
  5. 2021A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authenticationcited by 7
  6. 2022SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognitioncited by 7
  7. 2022Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identificationcited by 6
  8. 2020Analysis of Dominant Classes in Universal Adversarial Perturbationscited by 4
  9. 2022Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetoothcited by 4
  10. 2023Over-The-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprintingcited by 4
  11. 2023White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identificationcited by 2
  12. 2024DeepCRF: Deep Learning-Enhanced CSI-Based RF Fingerprinting for Channel-Resilient WiFi Device Identificationcited by 1
  13. 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learningcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authenticationcited by 0
  2. 2026Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learningcited by 0