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 73 篇，第 1 / 2 页

1. ICML 2024$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
2. ICML 2024Adversarial Attacks on Combinatorial Multi-Armed Bandits
3. ICML 2024Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
4. ICML 2024Ai-sampler: Adversarial Learning of Markov kernels with involutive maps
5. ICML 2024Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data
6. ICML 2024BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
7. ICML 2024Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning
8. ICML 2024Benign Overfitting in Adversarial Training of Neural Networks
9. ICML 2024Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
10. ICML 2024Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning
11. ICML 2024Can Implicit Bias Imply Adversarial Robustness?
12. ICML 2024Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
13. ICML 2024Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data
14. ICML 2024Controllable Prompt Tuning For Balancing Group Distributional Robustness
15. ICML 2024CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
16. ICML 2024DataFreeShield: Defending Adversarial Attacks without Training Data
17. ICML 2024Deep Networks Always Grok and Here is Why
18. ICML 2024Density Ratio Estimation with Doubly Strong Robustness
19. ICML 2024Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance
20. ICML 2024Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
21. ICML 2024Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
22. ICML 2024Enhancing Adversarial Robustness in SNNs with Sparse Gradients
23. ICML 2024Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples
24. ICML 2024Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
25. ICML 2024Fast Adversarial Attacks on Language Models In One GPU Minute
26. ICML 2024Fast White-Box Adversarial Streaming Without a Random Oracle
27. ICML 2024Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
28. ICML 2024Graph Adversarial Diffusion Convolution
29. ICML 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
30. ICML 2024How Smooth Is Attention?
31. ICML 2024Imitation Learning in Discounted Linear MDPs without exploration assumptions
32. ICML 2024Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
33. ICML 2024Improving Adversarial Energy-Based Model via Diffusion Process
34. ICML 2024Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting
35. ICML 2024Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
36. ICML 2024IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics
37. ICML 2024Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
38. ICML 2024Multigroup Robustness
39. ICML 2024No Free Prune: Information-Theoretic Barriers to Pruning at Initialization
40. ICML 2024Non-clairvoyant Scheduling with Partial Predictions
41. ICML 2024On the Duality Between Sharpness-Aware Minimization and Adversarial Training
42. ICML 2024Online Learning in CMDPs: Handling Stochastic and Adversarial Constraints
43. ICML 2024Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning
44. ICML 2024OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift
45. ICML 2024Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training
46. ICML 2024Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
47. ICML 2024Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
48. ICML 2024Provably Better Explanations with Optimized Aggregation of Feature Attributions
49. ICML 2024Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
50. ICML 2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders

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。