{"total":74,"rows":[{"id":"2102.01356","title":"Recent Advances in Adversarial Training for Adversarial Robustness","created":"2021-02-02","cited_by_count":279,"area":null},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2110.03825","title":"Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks","created":"2021-10-07","cited_by_count":74,"area":null},{"id":"2103.13127","title":"Black-box Detection of Backdoor Attacks with Limited Information and Data","created":"2021-03-24","cited_by_count":73,"area":null},{"id":"2102.04716","title":"Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training","created":"2021-02-09","cited_by_count":59,"area":null},{"id":"2106.01606","title":"Exploring Memorization in Adversarial Training","created":"2021-06-03","cited_by_count":55,"area":null},{"id":"2210.03895","title":"ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints","created":"2022-10-08","cited_by_count":48,"area":null},{"id":"2308.16258","title":"Robust Principles: Architectural Design Principles for Adversarially Robust CNNs","created":"2023-08-30","cited_by_count":46,"area":null},{"id":"2203.06020","title":"Enhancing Adversarial Training with Second-Order Statistics of Weights","created":"2022-03-11","cited_by_count":42,"area":null},{"id":"2202.13437","title":"A Unified Wasserstein Distributional Robustness Framework for Adversarial Training","created":"2022-02-27","cited_by_count":36,"area":null},{"id":"2303.10653","title":"Randomized Adversarial Training via Taylor Expansion","created":"2023-03-19","cited_by_count":28,"area":null},{"id":"2210.15291","title":"Isometric 3D Adversarial Examples in the Physical World","created":"2022-10-27","cited_by_count":25,"area":null},{"id":"2111.13844","title":"Adaptive Image Transformations for Transfer-based Adversarial Attack","created":"2021-11-27","cited_by_count":22,"area":null},{"id":"2307.10235","title":"Towards Viewpoint-Invariant Visual Recognition via Adversarial Training","created":"2023-07-16","cited_by_count":19,"area":null},{"id":"2212.01806","title":"Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks","created":"2022-12-04","cited_by_count":18,"area":null},{"id":"2303.13846","title":"Feature Separation and Recalibration for Adversarial Robustness","created":"2023-03-24","cited_by_count":16,"area":null},{"id":"2306.09844","title":"Wasserstein distributional robustness of neural networks","created":"2023-06-16","cited_by_count":16,"area":null},{"id":"2209.11964","title":"Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning","created":"2022-09-24","cited_by_count":15,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2101.06309","title":"Fundamental Tradeoffs in Distributionally Adversarial Training","created":"2021-01-15","cited_by_count":12,"area":null},{"id":"2102.01886","title":"Learning Diverse-Structured Networks for Adversarial Robustness","created":"2021-02-03","cited_by_count":12,"area":null},{"id":"2203.00553","title":"Global-Local Regularization Via Distributional Robustness","created":"2022-03-01","cited_by_count":11,"area":null},{"id":"2309.05145","title":"Outlier Robust Adversarial Training","created":"2023-09-10","cited_by_count":10,"area":null},{"id":"2102.11731","title":"Rethinking Natural Adversarial Examples for Classification Models","created":"2021-02-23","cited_by_count":9,"area":null},{"id":"2203.04713","title":"Defending Black-box Skeleton-based Human Activity Classifiers","created":"2022-03-09","cited_by_count":9,"area":null},{"id":"2211.10752","title":"Towards Robust Dataset Learning","created":"2022-11-19","cited_by_count":9,"area":null},{"id":"2306.14126","title":"Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training","created":"2023-06-25","cited_by_count":9,"area":null},{"id":"2312.07067","title":"Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training","created":"2023-12-12","cited_by_count":9,"area":null},{"id":"2109.06294","title":"On the regularized risk of distributionally robust learning over deep neural networks","created":"2021-09-13","cited_by_count":8,"area":null},{"id":"2110.11950","title":"Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff","created":"2021-10-22","cited_by_count":8,"area":null},{"id":"2208.05650","title":"Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks","created":"2022-08-11","cited_by_count":7,"area":null},{"id":"2307.11528","title":"Improving Viewpoint Robustness for Visual Recognition via Adversarial Training","created":"2023-07-21","cited_by_count":7,"area":null},{"id":"2512.08176","title":"Worst-case generation via minimax optimization in Wasserstein space","created":"2025-12-09","cited_by_count":7,"area":null},{"id":"2104.10586","title":"Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations","created":"2021-04-21","cited_by_count":6,"area":null},{"id":"2112.08091","title":"Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems","created":"2021-12-15","cited_by_count":6,"area":null},{"id":"2301.03110","title":"RobArch: Designing Robust Architectures against Adversarial Attacks","created":"2023-01-08","cited_by_count":6,"area":null},{"id":"2410.15042","title":"Adversarial Training: A Survey","created":"2024-10-19","cited_by_count":6,"area":null},{"id":"2503.01924","title":"TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions","created":"2025-03-02","cited_by_count":6,"area":null},{"id":"2106.12900","title":"Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks","created":"2021-06-22","cited_by_count":4,"area":null},{"id":"2109.00678","title":"Regional Adversarial Training for Better Robust Generalization","created":"2021-09-02","cited_by_count":4,"area":null},{"id":"2503.12793","title":"Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization","created":"2025-03-17","cited_by_count":4,"area":null},{"id":"2109.01766","title":"SEC4SR: A Security Analysis Platform for Speaker Recognition","created":"2021-09-04","cited_by_count":3,"area":null},{"id":"2110.08042","title":"Adversarial Attacks on ML Defense Models Competition","created":"2021-10-15","cited_by_count":3,"area":null},{"id":"2110.08256","title":"Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness","created":"2021-10-13","cited_by_count":3,"area":null},{"id":"2111.15603","title":"Human Imperceptible Attacks and Applications to Improve Fairness","created":"2021-11-30","cited_by_count":3,"area":null},{"id":"2206.03727","title":"Wavelet Regularization Benefits Adversarial Training","created":"2022-06-08","cited_by_count":3,"area":null},{"id":"2302.02092","title":"Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics","created":"2023-02-04","cited_by_count":3,"area":null},{"id":"2306.16131","title":"Distributional Modeling for Location-Aware Adversarial Patches","created":"2023-06-28","cited_by_count":3,"area":null},{"id":"2403.05100","title":"Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume","created":"2024-03-08","cited_by_count":3,"area":null},{"id":"2408.10647","title":"Privacy-preserving Universal Adversarial Defense for Black-box Models","created":"2024-08-20","cited_by_count":3,"area":null},{"id":"2408.14875","title":"Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures","created":"2024-08-27","cited_by_count":3,"area":null},{"id":"2412.11066","title":"Learning Robust and Privacy-Preserving Representations via Information Theory","created":"2024-12-15","cited_by_count":3,"area":null},{"id":"2309.03791","title":"Optimal Transport Regularized Divergences: Application to Adversarial Robustness","created":"2023-09-07","cited_by_count":2,"area":null},{"id":"2311.01356","title":"Upper and lower bounds for the Lipschitz constant of random neural networks","created":"2023-11-02","cited_by_count":2,"area":null},{"id":"2312.10587","title":"E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning","created":"2023-12-17","cited_by_count":2,"area":null},{"id":"2406.15104","title":"Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors","created":"2024-06-21","cited_by_count":2,"area":null},{"id":"2411.02871","title":"Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2602.01427","title":"Robust Generalization with Adaptive Optimal Transport Priors for Decision-Focused Learning","created":"2026-02-01","cited_by_count":2,"area":null},{"id":"2108.09713","title":"Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations","created":"2021-08-22","cited_by_count":1,"area":null},{"id":"2405.18499","title":"Training More Robust Classification Model via Discriminative Loss and Gaussian Noise Injection","created":"2024-05-28","cited_by_count":1,"area":null},{"id":"2502.09352","title":"Wasserstein distributional adversarial training for deep neural networks","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2504.08414","title":"Adversarial Examples in Environment Perception for Automated Driving (Review)","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2602.20624","title":"Physics-based phenomenological characterization of cross-modal bias in multimodal models","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2605.07690","title":"Fortifying Time Series: DTW-Certified Robust Anomaly Detection","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2110.07801","title":"Adversarial Purification through Representation Disentanglement","created":"2021-10-15","cited_by_count":0,"area":null},{"id":"2202.09446","title":"Learning Representations Robust to Group Shifts and Adversarial Examples","created":"2022-02-18","cited_by_count":0,"area":null},{"id":"2203.09790","title":"Towards Robust 2D Convolution for Reliable Visual Recognition","created":"2022-03-18","cited_by_count":0,"area":null},{"id":"2304.11043","title":"Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2506.15685","title":"Ignition Phase : Standard Training for Fast Adversarial Robustness","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2508.06127","title":"SAM Encoder Breach by Adversarial Simplicial Complex Triggers Downstream Model Failures","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2601.13645","title":"Quadratic Upper Bound for Boosting Robustness","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2603.27871","title":"Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences","created":"2026-03-29","cited_by_count":0,"area":null},{"id":"2607.01305","title":"Generative AI and Federated Learning for Intrusion Detection Systems: A Survey","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.18186","title":"What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems","created":"2026-08-18","cited_by_count":0,"area":null}],"next_cursor":null}