mlsanityData to 7 Oct 2026

Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems

arXiv 1902.08391 ↗First posted 22 Feb 2019cs.IT cs.CR cs.LGcited by 19

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Abstract (arXiv)

We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we elaborate how an attacker can craft effective physical black-box adversarial attacks. Due to the openness (broadcast nature) of the wireless channel, an adversary transmitter can increase the block-error-rate of a communication system by orders of magnitude by transmitting a well-designed perturbation signal over the channel. We reveal that the adversarial attacks are more destructive than jamming attacks. We also show that classical coding schemes are more robust than autoencoders against both adversarial and jamming attacks. The codes are available at [1].

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2019.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 2; 2024: 1; 2025: 1; 2026: 2.

19 papers.

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  1. 2021A Unified Framework for Adversarial Attack and Defense in Constrained Feature Spacecited by 22
  2. 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
  3. 2021Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Networkcited by 11
  4. 2022Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attackscited by 10
  5. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  6. 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
  7. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  8. 2022Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examplescited by 5
  9. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 4
  10. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  11. 2021Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenescited by 3
  12. 2023Adversarial Robustness in Unsupervised Machine Learning: A Systematic Reviewcited by 3
7 more
  1. 2025Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identificationcited by 2
  2. 2022Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Trainingcited by 1
  3. 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systemscited by 1
  4. 2024Enhancing O-RAN Security: Evasion Attacks and Robust Defenses for Graph Reinforcement Learning-based Connection Managementcited by 1
  5. 2021A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attackscited by 0
  6. 2026On the Fragility of AI-Based Channel Decoders under Small Channel Perturbationscited by 0
  7. 2026QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authenticationcited by 0