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 422 篇，第 1 / 9 页

1. NeurIPS 2026A Little Robustness Is All You Need: Leveraging Predictions for Contextual Optimization
2. NeurIPS 2026A Margin Perspective on LoRA: Robustness to Catastrophic Forgetting and Adapter Merging (MaLoRA)
3. NeurIPS 2026A Regularization-Based Approach to Public Belief State Search for Adversarial Games
4. NeurIPS 2026A Retained-Signal Interface for LLM Watermark Robustness under Paraphrase
5. NeurIPS 2026A Simple Class-Agnostic Approach to Enhance Fair Adversarial Training
6. NeurIPS 2026A Solver-Efficient Neural Adversarial Attack on Subgraph Matching Models
7. NeurIPS 2026Adversarial Corpus Selection to Attack Subgraph Matching based Graph Retrieval
8. NeurIPS 2026Adversarial Group Fairness in Contextual Bandits: When Robust is Not Fair
9. NeurIPS 2026AI Models Can Provably Hide Arbitrary Capabilities
10. NeurIPS 2026AnchorRep: Defending LLMs Against Cross-Model Adversarial Transfer via Representation Repulsion
11. NeurIPS 2026Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
12. NeurIPS 2026BayesAT: Bayes-Guided Progressive Distillation for Semi-Supervised Adversarial Training
13. NeurIPS 2026Benchmarking Sensor-Fault Robustness in Forecasting
14. NeurIPS 2026Beyond Correctness: Robustness-Driven Evolutionary Self-Training for Large Language Models
15. NeurIPS 2026Beyond Ground Truth: Evaluating Non-Verifiable Reasoning in LLMs through Moral Robustness
16. NeurIPS 2026BodyBench: Evaluating Adversarial Image Defenses Against AI Nudification Inpainting
17. NeurIPS 2026Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
18. NeurIPS 2026Brittlebench: Quantifying LLM robustness via prompt sensitivity
19. NeurIPS 2026Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization
20. NeurIPS 2026Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
21. NeurIPS 2026Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning
22. NeurIPS 2026D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Amplitude and Pixel Spaces
23. NeurIPS 2026DECEIVE-AFC: Adversarial Claim Attacks against Search-Enabled LLM-based Fact-Checking Systems
24. NeurIPS 2026Distributionally Robust Mixture-of-Experts Training
25. NeurIPS 2026Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness
26. NeurIPS 2026Do Robust LVLMs Hallucinate More? Uncovering Robustness-Induced Hallucination in Large Vision-Language Models
27. NeurIPS 2026Embedding Security Properties into AI-Enabled Cyber-Physical Systems
28. NeurIPS 2026Even Sailors Need Calm Seas: Taming the Geometry of VLMs for Fast Adversarial Fine-Tuning
29. NeurIPS 2026Exploiting Fine-Tuning Structures to Improve Adversarial Transferability on Downstream SAM
30. NeurIPS 2026Fingerprinting Inference Systems of Large Language Models
31. NeurIPS 2026FocusBranch: Combinatorial Branch-and-Bound for $\ell_0$ Neural Network Robustness Verification
32. NeurIPS 2026From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
33. NeurIPS 2026From Tokens to Tactics: Adversarial Text Optimization in an Axis-Aligned Rhetorical Strategy Space for Harmful Content Detection
34. NeurIPS 2026FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models
35. NeurIPS 2026G-PAC: Constructing Cohesive Pseudo-Features for Generalizable Physical Adversarial Camouflage
36. NeurIPS 2026Improving Context-Shift Robustness of Convolutional Networks via Context-Regularized Cross-Entropy
37. NeurIPS 2026Joint Certification for Attributed Graphs: Beyond Topology-Only Robustness
38. NeurIPS 2026Learning Better Certified Models from Empirically-Robust Teachers
39. NeurIPS 2026Learning Data-free Universal Adversarial Perturbation with Hybrid Priors and Gradient-Guided Sharpness Regularization
40. NeurIPS 2026Learning Recoverable Neural Networks against Weight Corruption via Simple Zero-Sum Projection
41. NeurIPS 2026Learning Robust Reasoning through Guided Adversarial Self-Play
42. NeurIPS 2026Learning Robust Representations for Defending White-Box Adversarial Attacks in Continual Learning
43. NeurIPS 2026Learning-Augmented Online Portfolio Selection: Optimal Robustness-Consistency Tradeoffs
44. NeurIPS 2026Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness
45. NeurIPS 2026LPS-Bench: Benchmarking Safety Awareness of Computer-Use Agents in Long-Horizon Planning under Benign and Adversarial Scenarios
46. NeurIPS 2026Malicious Node Injection: A Transferable Adversarial Attack on GNN Fairness
47. NeurIPS 2026Manifold-Aligned Adversarial Perturbation for Anti-Customization under Diffusion-based Purification
48. NeurIPS 2026Measuring Robustness and Efficiency in a Connectome-Constrained Fly Visual System Model on a Collision-Detection Task
49. NeurIPS 2026My Video Stays Mine: Temporally Consistent Universal Adversarial Perturbations against Video Customization
50. NeurIPS 2026Natural Jammers: When Non-Robust Features Become Antagonistic

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。