Adversarial Robustness papers, 2023–2026

Key findings

• Adversarial Robustness went from 313 papers in 2023 to 317 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Adversarial Robustness work: 422 papers from 2023 to 2026, 33% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 13 in 2023 to 56 in 2026.
• Among the 5 topics under Security and Trust, it ranks #1 by 2026 paper count; the fastest grower in the group, Watermarking, is up 9.3x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Adversarial Robustness papers

313 papers, page 5 of 7

201. ICML 2023Robust Perception through Equivariance
202. ICML 2023Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
203. ICML 2023Robustness in Multimodal Learning under Train-Test Modality Mismatch
204. ICML 2023Run-off Election: Improved Provable Defense against Data Poisoning Attacks
205. ICML 2023Simple and Fast Group Robustness by Automatic Feature Reweighting
206. ICML 2023Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
207. ICML 2023Stratified Adversarial Robustness with Rejection
208. ICML 2023The multimarginal optimal transport formulation of adversarial multiclass classification
209. ICML 2023Towards Reliable Neural Specifications
210. ICML 2023Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks
211. ICML 2023Training-Free Neural Active Learning with Initialization-Robustness Guarantees
212. ICML 2023Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer
213. ICML 2023Understanding the Impact of Adversarial Robustness on Accuracy Disparity
214. NeurIPS 2023A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
215. NeurIPS 2023Adversarial Examples Are Not Real Features
216. NeurIPS 2023Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
217. NeurIPS 2023Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
218. NeurIPS 2023Adversarial Learning for Feature Shift Detection and Correction
219. NeurIPS 2023Adversarial Resilience in Sequential Prediction via Abstention
220. NeurIPS 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
221. NeurIPS 2023Adversarial Robustness through Random Weight Sampling
222. NeurIPS 2023Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation
223. NeurIPS 2023Adversarial Training from Mean Field Perspective
224. NeurIPS 2023AND: Adversarial Neural Degradation for Learning Blind Image Super-Resolution
225. NeurIPS 2023Asymmetric Certified Robustness via Feature-Convex Neural Networks
226. NeurIPS 2023Asynchronous Proportional Response Dynamics: Convergence in Markets with Adversarial Scheduling
227. NeurIPS 2023Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
228. NeurIPS 2023Batchnorm Allows Unsupervised Radial Attacks
229. NeurIPS 2023Benchmarking Robustness to Adversarial Image Obfuscations
230. NeurIPS 2023Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
231. NeurIPS 2023Blurred-Dilated Method for Adversarial Attacks
232. NeurIPS 2023Boosting Adversarial Transferability by Achieving Flat Local Maxima
233. NeurIPS 2023Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
234. NeurIPS 2023CamoPatch: An Evolutionary Strategy for Generating Camoflauged Adversarial Patches
235. NeurIPS 2023Certifiably Robust Graph Contrastive Learning
236. NeurIPS 2023Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
237. NeurIPS 2023Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker
238. NeurIPS 2023Color Equivariant Convolutional Networks
239. NeurIPS 2023Connecting Certified and Adversarial Training
240. NeurIPS 2023Content-based Unrestricted Adversarial Attack
241. NeurIPS 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
242. NeurIPS 2023Designing Robust Transformers using Robust Kernel Density Estimation
243. NeurIPS 2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
244. NeurIPS 2023Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
245. NeurIPS 2023Distribution Learnability and Robustness
246. NeurIPS 2023Dynamic Regret of Adversarial Linear Mixture MDPs
247. NeurIPS 2023Dynamically Masked Discriminator for GANs
248. NeurIPS 2023Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
249. NeurIPS 2023Efficient Testable Learning of Halfspaces with Adversarial Label Noise
250. NeurIPS 2023Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization

FAQ

How many Adversarial Robustness papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,291 in total: 313 in 2023, 334 in 2024, 327 in 2025, 317 in 2026.

How many Adversarial Robustness papers are at NeurIPS 2026?

NeurIPS 2026 has 81 Adversarial Robustness papers, versus 110 the year before.

How many Adversarial Robustness papers are at ICLR 2026?

ICLR 2026 has 94 Adversarial Robustness papers, versus 88 the year before.

How many Adversarial Robustness papers are at ICML 2026?

ICML 2026 has 93 Adversarial Robustness papers, versus 85 the year before.

How many Adversarial Robustness papers are at CVPR 2026?

CVPR 2026 has 49 Adversarial Robustness papers, versus 44 the year before.

Which conference publishes the most Adversarial Robustness papers?

NeurIPS, with 422 papers from 2023 to 2026.