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 313 篇，第 2 / 7 页

51. CVPR 2023Revisiting Residual Networks for Adversarial Robustness
52. CVPR 2023RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts
53. CVPR 2023Robust Dynamic Radiance Fields
54. CVPR 2023Robust Single Image Reflection Removal Against Adversarial Attacks
55. CVPR 2023Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
56. CVPR 2023Semi-Supervised Hand Appearance Recovery via Structure Disentanglement and Dual Adversarial Discrimination
57. CVPR 2023Sibling-Attack: Rethinking Transferable Adversarial Attacks Against Face Recognition
58. CVPR 2023SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples
59. CVPR 2023StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
60. CVPR 2023StyLess: Boosting the Transferability of Adversarial Examples
61. CVPR 2023Super-CLEVR: A Virtual Benchmark To Diagnose Domain Robustness in Visual Reasoning
62. CVPR 2023Teacher-Generated Spatial-Attention Labels Boost Robustness and Accuracy of Contrastive Models
63. CVPR 2023TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
64. CVPR 2023The Best Defense Is a Good Offense: Adversarial Augmentation Against Adversarial Attacks
65. CVPR 2023The Enemy of My Enemy Is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
66. CVPR 2023Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
67. CVPR 2023Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
68. CVPR 2023Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
69. CVPR 2023Towards Transferable Targeted Adversarial Examples
70. CVPR 2023Trade-Off Between Robustness and Accuracy of Vision Transformers
71. CVPR 2023Turning Strengths Into Weaknesses: A Certified Robustness Inspired Attack Framework Against Graph Neural Networks
72. CVPR 2023TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization
73. CVPR 2023Understanding the Robustness of 3D Object Detection With Bird's-Eye-View Representations in Autonomous Driving
74. CVPR 2023Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor
75. ICLR 2023(Certified!!) Adversarial Robustness for Free!
76. ICLR 2023A law of adversarial risk, interpolation, and label noise
77. ICLR 2023A Unified Algebraic Perspective on Lipschitz Neural Networks
78. ICLR 2023Adversarial Attacks on Adversarial Bandits
79. ICLR 2023Adversarial Imitation Learning with Preferences
80. ICLR 2023Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
81. ICLR 2023AGRO: Adversarial discovery of error-prone Groups for Robust Optimization
82. ICLR 2023Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness
83. ICLR 2023Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
84. ICLR 2023Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts
85. ICLR 2023Boosting Adversarial Transferability using Dynamic Cues
86. ICLR 2023Brain-like representational straightening of natural movies in robust feedforward neural networks
87. ICLR 2023Can CNNs Be More Robust Than Transformers?
88. ICLR 2023Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
89. ICLR 2023Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation
90. ICLR 2023Certified Training: Small Boxes are All You Need
91. ICLR 2023Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization
92. ICLR 2023Data augmentation alone can improve adversarial training
93. ICLR 2023DAVA: Disentangling Adversarial Variational Autoencoder
94. ICLR 2023Defending against Adversarial Audio via Diffusion Model
95. ICLR 2023DensePure: Understanding Diffusion Models for Adversarial Robustness
96. ICLR 2023Distributionally Robust Post-hoc Classifiers under Prior Shifts
97. ICLR 2023Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement
98. ICLR 2023Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
99. ICLR 2023Efficient Certified Training and Robustness Verification of Neural ODEs
100. ICLR 2023Empowering Graph Representation Learning with Test-Time Graph Transformation

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。