mlsanityData to 7 Oct 2026

Adversarial Training: A Survey

arXiv 2410.15042 ↗First posted 19 Oct 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

Adversarial training (AT) refers to integrating adversarial examples -- inputs altered with imperceptible perturbations that can significantly impact model predictions -- into the training process. Recent studies have demonstrated the effectiveness of AT in improving the robustness of deep neural networks against diverse adversarial attacks. However, a comprehensive overview of these developments is still missing. This survey addresses this gap by reviewing a broad range of recent and representative studies. Specifically, we first describe the implementation procedures and practical applications of AT, followed by a comprehensive review of AT techniques from three perspectives: data enhancement, network design, and training configurations. Lastly, we discuss common challenges in AT and propose several promising directions for future research.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 207 of its 267 references that are in the map. First posted on arXiv 19 Oct 2024.

Builds on

207 of 267 references.

References · 207

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38 more
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50 most cited of 207 All 207 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Modelscited by 9
  2. 2025Universal and Transferable Attacks on Pathology Foundation Modelscited by 2
  3. 2025Divided We Fall: Defending Against Adversarial Attacks via Soft-Gated Fractional Mixture-of-Experts with Randomized Adversarial Trainingcited by 1
  4. 2025NAPPure: Adversarial Purification for Robust Image Classification under Non-Additive Perturbationscited by 0
  5. 2026Streaming Adversarial Robustness in Fuzzy ARTMAP: Mechanism-Aligned Evaluation, Progressive Training, and Interpretable Diagnosticscited by 0
  6. 2026Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogatescited by 0