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 60 篇，第 1 / 2 页

1. ICML 2023A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification
2. ICML 2023A Law of Robustness beyond Isoperimetry
3. ICML 2023ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
4. ICML 2023Adversarial Cheap Talk
5. ICML 2023Adversarial Classification: Necessary Conditions and Geometric Flows
6. ICML 2023Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
7. ICML 2023Adversarial Parameter Attack on Deep Neural Networks
8. ICML 2023Adversarial Policies Beat Superhuman Go AIs
9. ICML 2023Adversarial robustness of amortized Bayesian inference
10. ICML 2023Adversarially Robust PAC Learnability of Real-Valued Functions
11. ICML 2023Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
12. ICML 2023BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
13. ICML 2023Computational Asymmetries in Robust Classification
14. ICML 2023CrossSplit: Mitigating Label Noise Memorization through Data Splitting
15. ICML 2023Delayed Bandits: When Do Intermediate Observations Help?
16. ICML 2023Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
17. ICML 2023Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing
18. ICML 2023Directed Chain Generative Adversarial Networks
19. ICML 2023Do Perceptually Aligned Gradients Imply Robustness?
20. ICML 2023Doubly Adversarial Federated Bandits
21. ICML 2023Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
22. ICML 2023Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration
23. ICML 2023Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
24. ICML 2023Federated Adversarial Learning: A Framework with Convergence Analysis
25. ICML 2023From Robustness to Privacy and Back
26. ICML 2023GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
27. ICML 2023How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy
28. ICML 2023Identification of the Adversary from a Single Adversarial Example
29. ICML 2023Improved Algorithms for White-Box Adversarial Streams
30. ICML 2023Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
31. ICML 2023Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics
32. ICML 2023Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
33. ICML 2023Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints
34. ICML 2023Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
35. ICML 2023Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
36. ICML 2023MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
37. ICML 2023NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
38. ICML 2023On the Robustness of Randomized Ensembles to Adversarial Perturbations
39. ICML 2023On the Robustness of Text Vectorizers
40. ICML 2023One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
41. ICML 2023Online Nonstochastic Control with Adversarial and Static Constraints
42. ICML 2023Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
43. ICML 2023Phase-aware Adversarial Defense for Improving Adversarial Robustness
44. ICML 2023Probabilistic Categorical Adversarial Attack and Adversarial Training
45. ICML 2023Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
46. ICML 2023Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
47. ICML 2023Refined Regret for Adversarial MDPs with Linear Function Approximation
48. ICML 2023Robust Perception through Equivariance
49. ICML 2023Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
50. ICML 2023Robustness in Multimodal Learning under Train-Test Modality Mismatch

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。