对抗鲁棒论文趋势 2023–2026
对抗样本与防御
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 1,291 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 100 | 79 | 60 | 74 | 313 |
| 2024 | 131 | 70 | 73 | 60 | 334 |
| 2025 | 110 | 88 | 85 | 44 | 327 |
| 2026 | 81 | 94 | 93 | 49 | 317 |
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 334 篇,第 5 / 7 页
- ICML 2024Uniformly Stable Algorithms for Adversarial Training and Beyond
- ICML 2024VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
- ICML 2024WAVES: Benchmarking the Robustness of Image Watermarks
- NeurIPS 2024A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
- NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
- NeurIPS 2024A Sober Look at the Robustness of CLIPs to Spurious Features
- NeurIPS 2024A StrongREJECT for Empty Jailbreaks
- NeurIPS 2024Achievable distributional robustness when the robust risk is only partially identified
- NeurIPS 2024Achieving Domain-Independent Certified Robustness via Knowledge Continuity
- NeurIPS 2024AdanCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer
- NeurIPS 2024Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
- NeurIPS 2024AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks
- NeurIPS 2024Adversarial Environment Design via Regret-Guided Diffusion Models
- NeurIPS 2024Adversarial Moment-Matching Distillation of Large Language Models
- NeurIPS 2024Adversarial Schrödinger Bridge Matching
- NeurIPS 2024Adversarially Robust Decision Transformer
- NeurIPS 2024Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters
- NeurIPS 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- NeurIPS 2024An Analysis of Robustness of Non-Lipschitz Networks
- NeurIPS 2024AudioMarkBench: Benchmarking Robustness of Audio Watermarking
- NeurIPS 2024Back to the Continuous Attractor
- NeurIPS 2024BAN: Detecting Backdoors Activated by Adversarial Neuron Noise
- NeurIPS 2024Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
- NeurIPS 2024Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation
- NeurIPS 2024Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
- NeurIPS 2024Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
- NeurIPS 2024Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
- NeurIPS 2024Can Models Learn Skill Composition from Examples?
- NeurIPS 2024CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
- NeurIPS 2024Certified Adversarial Robustness via Randomized $\alpha$-Smoothing for Regression Models
- NeurIPS 2024Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing
- NeurIPS 2024Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness
- NeurIPS 2024Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
- NeurIPS 2024Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective
- NeurIPS 2024Cross-Modality Perturbation Synergy Attack for Person Re-identification
- NeurIPS 2024DarkSAM: Fooling Segment Anything Model to Segment Nothing
- NeurIPS 2024DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain
- NeurIPS 2024Decoupled Kullback-Leibler Divergence Loss
- NeurIPS 2024Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
- NeurIPS 2024DeNetDM: Debiasing by Network Depth Modulation
- NeurIPS 2024DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms
- NeurIPS 2024Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers
- NeurIPS 2024DiffHammer: Rethinking the Robustness of Diffusion-Based Adversarial Purification
- NeurIPS 2024Diffusion Models are Certifiably Robust Classifiers
- NeurIPS 2024Diffusion-Reward Adversarial Imitation Learning
- NeurIPS 2024ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks
- NeurIPS 2024Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
- NeurIPS 2024Efficient Adversarial Training in LLMs with Continuous Attacks
- NeurIPS 2024Elliptical Attention
- NeurIPS 2024Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查对抗鲁棒在 NeurIPS 2023–2026 的论文趋势,哪一年涨得最快?
AI 会调用的工具:get_topic_stats {"topic": "adversarial-robustness"}
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。