对抗鲁棒论文趋势 2023–2026

主要结论

• 对抗鲁棒论文从 2023 年的 313 篇增长到 2026 年的 317 篇，三年间扩大到约 1.0 倍。
• NeurIPS 是发对抗鲁棒论文最多的会议，2023–2026 年共 422 篇，占四个会议总量的 33%。
• 交叉方向里涨得最快的是对齐与安全：同时属于两个主题的论文从 2023 年的 13 篇增至 2026 年的 56 篇。
• 在「安全与可信」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的水印是 9.3 倍，对抗鲁棒是 1.0 倍。

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

• NeurIPS 2026A Little Robustness Is All You Need: Leveraging Predictions for Contextual Optimization
• ICML 2026Bias in Zeroth-Order Normal Estimation for Decision-Based Attacks
• ICLR 2026CERTIFIED VS. EMPIRICAL ADVERSARIAL ROBUSTNESS VIA HYBRID CONVOLUTIONS WITH ATTENTION STOCHASTICITY
• CVPR 2026A Combination of Noise and Bilateral Filters Achieve Supralinear and
• NeurIPS 2025A Unified Approach to Submodular Maximization Under Noise
• ICML 2025Adversarial Perturbations Are Formed by Iteratively Learning Linear Combinations of the Right Singular Vectors of the Adversarial Jacobian
• ICLR 2025$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
• CVPR 2025A Unified, Resilient, and Explainable Adversarial Patch Detector
• NeurIPS 2024A StrongREJECT for Empty Jailbreaks
• ICML 2024Adversarial Attacks on Combinatorial Multi-Armed Bandits
• ICLR 2024An LLM can Fool Itself: A Prompt-Based Adversarial Attack
• CVPR 2024Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
• NeurIPS 2023A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
• ICML 2023A Law of Robustness beyond Isoperimetry
• ICLR 2023A law of adversarial risk, interpolation, and label noise
• CVPR 2023Adversarial Normalization: I Can Visualize Everything (ICE)
• NeurIPS 2026A Regularization-Based Approach to Public Belief State Search for Adversarial Games
• ICML 2026CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction
• ICLR 2026Conformal Robustness Control: A New Strategy for Robust Decision
• CVPR 2026A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large

全部对抗鲁棒论文

共 79 篇，第 2 / 2 页

51. ICLR 2023One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks
52. ICLR 2023Part-Based Models Improve Adversarial Robustness
53. ICLR 2023Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
54. ICLR 2023Prompting GPT-3 To Be Reliable
55. ICLR 2023Provable Defense Against Geometric Transformations
56. ICLR 2023Provable Robustness against Wasserstein Distribution Shifts via Input Randomization
57. ICLR 2023Provably Auditing Ordinary Least Squares in Low Dimensions
58. ICLR 2023Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
59. ICLR 2023Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
60. ICLR 2023Revisiting adapters with adversarial training
61. ICLR 2023Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective
62. ICLR 2023Revisiting Robustness in Graph Machine Learning
63. ICLR 2023Robust Algorithms on Adaptive Inputs from Bounded Adversaries
64. ICLR 2023Robust Explanation Constraints for Neural Networks
65. ICLR 2023Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
66. ICLR 2023Robustness to corruption in pre-trained Bayesian neural networks
67. ICLR 2023Sound Randomized Smoothing in Floating-Point Arithmetic
68. ICLR 2023Squeeze Training for Adversarial Robustness
69. ICLR 2023TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
70. ICLR 2023TextShield: Beyond Successfully Detecting Adversarial Sentences in text classification
71. ICLR 2023The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
72. ICLR 2023The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
73. ICLR 2023Toward Adversarial Training on Contextualized Language Representation
74. ICLR 2023Towards Robustness Certification Against Universal Perturbations
75. ICLR 2023Transferable Unlearnable Examples
76. ICLR 2023Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
77. ICLR 2023Understanding Zero-shot Adversarial Robustness for Large-Scale Models
78. ICLR 2023Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
79. ICLR 2023Why adversarial training can hurt robust accuracy

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。