对抗鲁棒论文趋势 2023–2026

对抗样本与防御

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,291 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

对抗鲁棒论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023100796074313
2024131707360334
2025110888544327
202681949349317

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

全部对抗鲁棒论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 422 篇，第 8 / 9 页

  1. NeurIPS 2023Designing Robust Transformers using Robust Kernel Density Estimation
  2. NeurIPS 2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
  3. NeurIPS 2023Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
  4. NeurIPS 2023Distribution Learnability and Robustness
  5. NeurIPS 2023Dynamic Regret of Adversarial Linear Mixture MDPs
  6. NeurIPS 2023Dynamically Masked Discriminator for GANs
  7. NeurIPS 2023Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
  8. NeurIPS 2023Efficient Testable Learning of Halfspaces with Adversarial Label Noise
  9. NeurIPS 2023Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
  10. NeurIPS 2023Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
  11. NeurIPS 2023Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
  12. NeurIPS 2023Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
  13. NeurIPS 2023Enhancing Adversarial Robustness via Score-Based Optimization
  14. NeurIPS 2023Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
  15. NeurIPS 2023Fantastic Robustness Measures: The Secrets of Robust Generalization
  16. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
  17. NeurIPS 2023Hierarchical Randomized Smoothing
  18. NeurIPS 2023HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
  19. NeurIPS 2023Improving Adversarial Robustness via Information Bottleneck Distillation
  20. NeurIPS 2023Improving Adversarial Transferability via Intermediate-level Perturbation Decay
  21. NeurIPS 2023Improving Robustness with Adaptive Weight Decay
  22. NeurIPS 2023LEACE: Perfect linear concept erasure in closed form
  23. NeurIPS 2023Learning Provably Robust Estimators for Inverse Problems via Jittering
  24. NeurIPS 2023Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis
  25. NeurIPS 2023MADG: Margin-based Adversarial Learning for Domain Generalization
  26. NeurIPS 2023Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness
  27. NeurIPS 2023Meta-Learning Adversarial Bandit Algorithms
  28. NeurIPS 2023Multi-scale Diffusion Denoised Smoothing
  29. NeurIPS 2023Non-adversarial training of Neural SDEs with signature kernel scores
  30. NeurIPS 2023On Evaluating Adversarial Robustness of Large Vision-Language Models
  31. NeurIPS 2023On Private and Robust Bandits
  32. NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
  33. NeurIPS 2023On the Adversarial Robustness of Out-of-distribution Generalization Models
  34. NeurIPS 2023On the Robustness of Mechanism Design under Total Variation Distance
  35. NeurIPS 2023On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
  36. NeurIPS 2023One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning
  37. NeurIPS 2023Optimal Transport Model Distributional Robustness
  38. NeurIPS 2023PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
  39. NeurIPS 2023Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
  40. NeurIPS 2023Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
  41. NeurIPS 2023QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks
  42. NeurIPS 2023Rethinking the Backward Propagation for Adversarial Transferability
  43. NeurIPS 2023Revisiting Adversarial Robustness Distillation from the Perspective of Robust Fairness
  44. NeurIPS 2023Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
  45. NeurIPS 2023Robust Data Valuation with Weighted Banzhaf Values
  46. NeurIPS 2023Robust Lipschitz Bandits to Adversarial Corruptions
  47. NeurIPS 2023Robust low-rank training via approximate orthonormal constraints
  48. NeurIPS 2023Robust Matrix Sensing in the Semi-Random Model
  49. NeurIPS 2023Robustness Guarantees for Adversarially Trained Neural Networks
  50. NeurIPS 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查对抗鲁棒在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "adversarial-robustness"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

常见问题

2023–2026 年四大会议一共有多少篇对抗鲁棒论文？
共 1,291 篇，按年份分别是 2023 年 313 篇、2024 年 334 篇、2025 年 327 篇、2026 年 317 篇。
NeurIPS 2026 有多少篇对抗鲁棒论文？
NeurIPS 2026 有 81 篇对抗鲁棒论文，上一届是 110 篇。
ICLR 2026 有多少篇对抗鲁棒论文？
ICLR 2026 有 94 篇对抗鲁棒论文，上一届是 88 篇。
ICML 2026 有多少篇对抗鲁棒论文？
ICML 2026 有 93 篇对抗鲁棒论文，上一届是 85 篇。
CVPR 2026 有多少篇对抗鲁棒论文？
CVPR 2026 有 49 篇对抗鲁棒论文，上一届是 44 篇。
哪个会议的对抗鲁棒论文最多？
NeurIPS，2023–2026 年共 422 篇。

「安全与可信」下的其他主题