{"total":88,"rows":[{"id":"2102.01356","title":"Recent Advances in Adversarial Training for Adversarial Robustness","created":"2021-02-02","cited_by_count":279,"area":null},{"id":"2110.01167","title":"Trustworthy AI: From Principles to Practices","created":"2021-10-04","cited_by_count":207,"area":null},{"id":"2401.17263","title":"Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks","created":"2024-01-30","cited_by_count":156,"area":"safety"},{"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":"2302.04237","title":"Black Box Adversarial Prompting for Foundation Models","created":"2023-02-08","cited_by_count":62,"area":"safety"},{"id":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57,"area":null},{"id":"2103.01208","title":"Mind the box: $l_1$-APGD for sparse adversarial attacks on image classifiers","created":"2021-03-01","cited_by_count":51,"area":null},{"id":"2101.09617","title":"A Comprehensive Evaluation Framework for Deep Model Robustness","created":"2021-01-24","cited_by_count":42,"area":null},{"id":"2103.04623","title":"Consistency Regularization for Adversarial Robustness","created":"2021-03-08","cited_by_count":42,"area":null},{"id":"2510.09263","title":"SynthID-Image: Image watermarking at internet scale","created":"2025-10-10","cited_by_count":31,"area":null},{"id":"2202.04235","title":"Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations","created":"2022-02-09","cited_by_count":26,"area":null},{"id":"2102.02145","title":"Adversarially Robust Learning with Unknown Perturbation Sets","created":"2021-02-03","cited_by_count":24,"area":null},{"id":"2105.12508","title":"Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers","created":"2021-05-26","cited_by_count":23,"area":null},{"id":"2302.10164","title":"Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts","created":"2023-02-20","cited_by_count":23,"area":null},{"id":"2112.05005","title":"Mutual Adversarial Training: Learning together is better than going alone","created":"2021-12-09","cited_by_count":17,"area":null},{"id":"2202.10627","title":"On the Effectiveness of Adversarial Training against Backdoor Attacks","created":"2022-02-22","cited_by_count":16,"area":null},{"id":"2202.06043","title":"RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation","created":"2022-02-12","cited_by_count":15,"area":null},{"id":"2208.13058","title":"Adversarial Robustness for Tabular Data through Cost and Utility Awareness","created":"2022-08-27","cited_by_count":15,"area":null},{"id":"2105.14785","title":"Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart","created":"2021-05-31","cited_by_count":14,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2203.03888","title":"ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation","created":"2022-03-08","cited_by_count":12,"area":null},{"id":"2205.14230","title":"Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction","created":"2022-05-27","cited_by_count":12,"area":null},{"id":"2112.06323","title":"Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses","created":"2021-12-12","cited_by_count":10,"area":null},{"id":"2210.00557","title":"Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis","created":"2022-10-02","cited_by_count":9,"area":null},{"id":"2204.13779","title":"Formulating Robustness Against Unforeseen Attacks","created":"2022-04-28","cited_by_count":8,"area":null},{"id":"2302.10980","title":"MultiRobustBench: Benchmarking Robustness Against Multiple Attacks","created":"2023-02-21","cited_by_count":7,"area":null},{"id":"2104.08323","title":"Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators","created":"2021-04-16","cited_by_count":6,"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":"2211.14669","title":"Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning","created":"2022-11-26","cited_by_count":6,"area":null},{"id":"2303.13401","title":"Optimization and Optimizers for Adversarial Robustness","created":"2023-03-23","cited_by_count":6,"area":null},{"id":"2311.15782","title":"Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence","created":"2023-11-27","cited_by_count":6,"area":null},{"id":"2102.08868","title":"Bridging the Gap Between Adversarial Robustness and Optimization Bias","created":"2021-02-17","cited_by_count":5,"area":null},{"id":"2201.12440","title":"Certifying Model Accuracy under Distribution Shifts","created":"2022-01-28","cited_by_count":5,"area":null},{"id":"2206.06737","title":"Adversarial Vulnerability of Randomized Ensembles","created":"2022-06-14","cited_by_count":5,"area":null},{"id":"2210.03150","title":"Towards Out-of-Distribution Adversarial Robustness","created":"2022-10-06","cited_by_count":5,"area":null},{"id":"2303.15409","title":"Class-Conditioned Transformation for Enhanced Robust Image Classification","created":"2023-03-27","cited_by_count":5,"area":null},{"id":"2311.17608","title":"Adversarial Robust Memory-Based Continual Learner","created":"2023-11-29","cited_by_count":5,"area":null},{"id":"2602.03284","title":"Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks","created":"2026-02-03","cited_by_count":5,"area":null},{"id":"2105.08037","title":"Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation","created":"2021-05-17","cited_by_count":4,"area":null},{"id":"2203.04886","title":"Reverse Engineering $\\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees","created":"2022-03-09","cited_by_count":4,"area":null},{"id":"2211.00269","title":"Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks","created":"2022-11-01","cited_by_count":4,"area":null},{"id":"2310.12793","title":"OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift","created":"2023-10-19","cited_by_count":4,"area":null},{"id":"2407.17797","title":"A Unified Understanding of Adversarial Vulnerability Regarding Unimodal Models and Vision-Language Pre-training Models","created":"2024-07-25","cited_by_count":4,"area":"mllm"},{"id":"2409.07321","title":"Module-wise Adaptive Adversarial Training for End-to-end Autonomous Driving","created":"2024-09-11","cited_by_count":4,"area":"driving"},{"id":"2103.15385","title":"Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training","created":"2021-03-29","cited_by_count":3,"area":null},{"id":"2110.14871","title":"Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks","created":"2021-10-28","cited_by_count":3,"area":null},{"id":"2206.12169","title":"AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems","created":"2022-06-24","cited_by_count":3,"area":null},{"id":"2210.00621","title":"Optimization for Robustness Evaluation beyond $\\ell_p$ Metrics","created":"2022-10-02","cited_by_count":3,"area":null},{"id":"2303.13326","title":"Decentralized Adversarial Training over Graphs","created":"2023-03-23","cited_by_count":3,"area":null},{"id":"2109.11803","title":"Local Intrinsic Dimensionality Signals Adversarial Perturbations","created":"2021-09-24","cited_by_count":2,"area":null},{"id":"2111.01996","title":"Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness","created":"2021-11-03","cited_by_count":2,"area":null},{"id":"2210.02577","title":"A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition","created":"2022-10-05","cited_by_count":2,"area":null},{"id":"2212.03069","title":"Multiple Perturbation Attack: Attack Pixelwise Under Different $\\ell_p$-norms For Better Adversarial Performance","created":"2022-12-05","cited_by_count":2,"area":null},{"id":"2303.01936","title":"Multi-Agent Adversarial Training Using Diffusion Learning","created":"2023-03-03","cited_by_count":2,"area":null},{"id":"2311.05143","title":"SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training","created":"2023-11-09","cited_by_count":2,"area":null},{"id":"2405.13324","title":"Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2502.04248","title":"Adapting to Evolving Adversaries with Regularized Continual Robust Training","created":"2025-02-06","cited_by_count":2,"area":null},{"id":"2508.01074","title":"Evading Data Provenance in Deep Neural Networks","created":"2025-08-01","cited_by_count":2,"area":null},{"id":"2203.08959","title":"Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training","created":"2022-03-16","cited_by_count":1,"area":null},{"id":"2206.06592","title":"Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training","created":"2022-06-14","cited_by_count":1,"area":null},{"id":"2210.09405","title":"Towards Generating Adversarial Examples on Mixed-type Data","created":"2022-10-17","cited_by_count":1,"area":null},{"id":"2311.17607","title":"Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation","created":"2023-11-29","cited_by_count":1,"area":null},{"id":"2401.11126","title":"CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications","created":"2024-01-20","cited_by_count":1,"area":null},{"id":"2402.06827","title":"RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness","created":"2024-02-09","cited_by_count":1,"area":null},{"id":"2405.01349","title":"Position: Towards Resilience Against Adversarial Examples","created":"2024-05-02","cited_by_count":1,"area":null},{"id":"2407.08678","title":"How to beat a Bayesian adversary","created":"2024-07-11","cited_by_count":1,"area":null},{"id":"2407.09251","title":"Deep Adversarial Defense Against Multilevel-Lp Attacks","created":"2024-07-12","cited_by_count":1,"area":null},{"id":"2410.03000","title":"Towards Generalized Certified Robustness with Multi-Norm Training","created":"2024-10-03","cited_by_count":1,"area":null},{"id":"2508.14079","title":"A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy","created":"2025-08-12","cited_by_count":1,"area":null},{"id":"2510.13361","title":"Generalist++: A Meta-learning Framework for Mitigating Trade-off in Adversarial Training","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2605.16720","title":"Compositional Adversarial Training for Robust Visual Watermarking","created":"2026-05-16","cited_by_count":1,"area":null},{"id":"2105.14710","title":"Robustifying $\\ell_\\infty$ Adversarial Training to the Union of Perturbation Models","created":"2021-05-31","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":"2209.08682","title":"Towards Robust Off-Policy Evaluation via Human Inputs","created":"2022-09-18","cited_by_count":0,"area":null},{"id":"2210.16346","title":"Improving Hyperspectral Adversarial Robustness Under Multiple Attacks","created":"2022-10-28","cited_by_count":0,"area":null},{"id":"2211.16093","title":"Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering","created":"2022-11-29","cited_by_count":0,"area":null},{"id":"2303.10225","title":"Bridging Models to Defend: A Population-Based Strategy for Robust Adversarial Defense","created":"2023-03-17","cited_by_count":0,"area":null},{"id":"2306.04756","title":"A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2306.15482","title":"Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets","created":"2023-06-27","cited_by_count":0,"area":null},{"id":"2306.16738","title":"Towards Optimal Randomized Strategies in Adversarial Example Game","created":"2023-06-29","cited_by_count":0,"area":null},{"id":"2309.16207","title":"Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks","created":"2023-09-28","cited_by_count":0,"area":null},{"id":"2309.17048","title":"On Continuity of Robust and Accurate Classifiers","created":"2023-09-29","cited_by_count":0,"area":null},{"id":"2501.02267","title":"Towards a constructive framework for control theory","created":"2025-01-04","cited_by_count":0,"area":null},{"id":"2603.00481","title":"Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems","created":"2026-02-28","cited_by_count":0,"area":null},{"id":"2607.06109","title":"RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations","created":"2026-07-07","cited_by_count":0,"area":"moe"},{"id":"2607.19855","title":"Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.26043","title":"Robust CurveMoE: Multi-Norm Adversarial Defense for Mixture-of-Experts Models via Mode Connectivity","created":"2026-08-26","cited_by_count":0,"area":"moe"},{"id":"2609.02440","title":"Towards One-for-All Robustness Across a Continuum of Threat Levels","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}