{"total":96,"rows":[{"id":"2303.06614","title":"Synthetic Experience Replay","created":"2023-03-12","cited_by_count":101,"area":null},{"id":"2302.02503","title":"Leaving Reality to Imagination: Robust Classification via Generated Datasets","created":"2023-02-05","cited_by_count":91,"area":null},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2303.09051","title":"Robust Evaluation of Diffusion-Based Adversarial Purification","created":"2023-03-16","cited_by_count":65,"area":null},{"id":"2209.07399","title":"A Light Recipe to Train Robust Vision Transformers","created":"2022-09-15","cited_by_count":64,"area":null},{"id":"2301.09879","title":"Data Augmentation Alone Can Improve Adversarial Training","created":"2023-01-24","cited_by_count":43,"area":null},{"id":"2405.20469","title":"Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images","created":"2024-05-30","cited_by_count":41,"area":null},{"id":"2404.09349","title":"Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies","created":"2024-04-14","cited_by_count":38,"area":null},{"id":"2212.05630","title":"DISCO: Adversarial Defense with Local Implicit Functions","created":"2022-12-11","cited_by_count":34,"area":null},{"id":"2212.11005","title":"Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective","created":"2022-12-21","cited_by_count":28,"area":null},{"id":"2210.05938","title":"Robust Models are less Over-Confident","created":"2022-10-12","cited_by_count":25,"area":null},{"id":"2203.15331","title":"CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters","created":"2022-03-29","cited_by_count":20,"area":null},{"id":"2301.12554","title":"Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing","created":"2023-01-29","cited_by_count":18,"area":null},{"id":"2303.11135","title":"TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization","created":"2023-03-20","cited_by_count":18,"area":null},{"id":"2402.02263","title":"MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers","created":"2024-02-03","cited_by_count":16,"area":null},{"id":"2209.14826","title":"Towards Lightweight Black-Box Attacks against Deep Neural Networks","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2304.10819","title":"Auditing and Generating Synthetic Data with Controllable Trust Trade-offs","created":"2023-04-21","cited_by_count":13,"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":"2301.12576","title":"Uncovering Adversarial Risks of Test-Time Adaptation","created":"2023-01-29","cited_by_count":12,"area":null},{"id":"2206.09628","title":"Diversified Adversarial Attacks based on Conjugate Gradient Method","created":"2022-06-20","cited_by_count":11,"area":null},{"id":"2304.06430","title":"Certified Zeroth-order Black-Box Defense with Robust UNet Denoiser","created":"2023-04-13","cited_by_count":11,"area":null},{"id":"2307.00823","title":"Understanding the Transferability of Representations via Task-Relatedness","created":"2023-07-03","cited_by_count":11,"area":null},{"id":"2307.07250","title":"Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning","created":"2023-07-14","cited_by_count":11,"area":null},{"id":"2406.18451","title":"Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers","created":"2024-06-26","cited_by_count":10,"area":null},{"id":"2204.02481","title":"Adversarial Robustness through the Lens of Convolutional Filters","created":"2022-04-05","cited_by_count":9,"area":null},{"id":"2207.04129","title":"How many perturbations break this model? Evaluating robustness beyond adversarial accuracy","created":"2022-07-08","cited_by_count":9,"area":null},{"id":"2210.08906","title":"A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities","created":"2022-10-17","cited_by_count":9,"area":null},{"id":"2210.12606","title":"Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games","created":"2022-10-23","cited_by_count":8,"area":null},{"id":"2210.14404","title":"Adversarial Purification with the Manifold Hypothesis","created":"2022-10-26","cited_by_count":8,"area":null},{"id":"2312.05508","title":"Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation","created":"2023-12-09","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":"2310.01330","title":"Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association","created":"2023-10-02","cited_by_count":7,"area":null},{"id":"2501.16971","title":"RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples","created":"2025-01-28","cited_by_count":7,"area":null},{"id":"2211.12624","title":"Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization","created":"2022-11-22","cited_by_count":6,"area":null},{"id":"2211.12713","title":"Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles","created":"2022-11-23","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":"2212.05917","title":"SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation","created":"2022-12-12","cited_by_count":6,"area":null},{"id":"2302.10722","title":"Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker","created":"2023-02-21","cited_by_count":6,"area":null},{"id":"2306.07197","title":"AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation","created":"2023-06-12","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":"2310.02480","title":"Splitting the Difference on Adversarial Training","created":"2023-10-03","cited_by_count":5,"area":null},{"id":"2206.12364","title":"On Certifying and Improving Generalization to Unseen Domains","created":"2022-06-24","cited_by_count":4,"area":null},{"id":"2306.14275","title":"Enhancing Adversarial Training via Reweighting Optimization Trajectory","created":"2023-06-25","cited_by_count":4,"area":null},{"id":"2310.08177","title":"Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization","created":"2023-10-12","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":"2405.05075","title":"Sparse-PGD: A Unified Framework for Sparse Adversarial Perturbations Generation","created":"2024-05-08","cited_by_count":4,"area":null},{"id":"2206.06496","title":"Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations","created":"2022-06-13","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":"2403.05100","title":"Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume","created":"2024-03-08","cited_by_count":3,"area":null},{"id":"2503.02169","title":"One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy","created":"2025-03-04","cited_by_count":3,"area":null},{"id":"2209.02684","title":"Bag of Tricks for FGSM Adversarial Training","created":"2022-09-06","cited_by_count":2,"area":null},{"id":"2212.07992","title":"Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks","created":"2022-12-15","cited_by_count":2,"area":null},{"id":"2302.01440","title":"Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications","created":"2023-02-02","cited_by_count":2,"area":null},{"id":"2304.09875","title":"GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models","created":"2023-04-19","cited_by_count":2,"area":null},{"id":"2306.13263","title":"Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity","created":"2023-06-23","cited_by_count":2,"area":null},{"id":"2311.15165","title":"Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off","created":"2023-11-26","cited_by_count":2,"area":null},{"id":"2403.08425","title":"Specification Overfitting in Artificial Intelligence","created":"2024-03-13","cited_by_count":2,"area":null},{"id":"2404.00362","title":"STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario","created":"2024-03-30","cited_by_count":2,"area":null},{"id":"2410.12671","title":"New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class","created":"2024-10-16","cited_by_count":2,"area":null},{"id":"2410.21952","title":"On the Robustness of Adversarial Training Against Uncertainty Attacks","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2206.07018","title":"Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion","created":"2022-06-14","cited_by_count":1,"area":null},{"id":"2208.08083","title":"Two Heads are Better than One: Robust Learning Meets Multi-branch Models","created":"2022-08-17","cited_by_count":1,"area":null},{"id":"2210.00062","title":"Learning Robust Kernel Ensembles with Kernel Average Pooling","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2210.08929","title":"DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers","created":"2022-10-17","cited_by_count":1,"area":null},{"id":"2306.16614","title":"Group-based Robustness: A General Framework for Customized Robustness in the Real World","created":"2023-06-29","cited_by_count":1,"area":null},{"id":"2310.11890","title":"IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks","created":"2023-10-18","cited_by_count":1,"area":null},{"id":"2312.03085","title":"ScAR: Scaling Adversarial Robustness for LiDAR Object Detection","created":"2023-12-05","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":"2406.03143","title":"ZeroPur: Succinct Training-Free Adversarial Purification","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2501.07251","title":"MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework","created":"2025-01-13","cited_by_count":1,"area":null},{"id":"2502.00834","title":"Boosting Adversarial Robustness and Generalization with Dictionary Structure","created":"2025-02-02","cited_by_count":1,"area":null},{"id":"2502.05542","title":"Democratic Training Against Universal Adversarial Perturbations","created":"2025-02-08","cited_by_count":1,"area":null},{"id":"2503.04853","title":"From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2504.10555","title":"Beyond the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domains","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2603.03507","title":"Solving adversarial examples requires solving exponential misalignment","created":"2026-03-03","cited_by_count":1,"area":null},{"id":"2204.01568","title":"DAD: Data-free Adversarial Defense at Test Time","created":"2022-04-04","cited_by_count":0,"area":null},{"id":"2205.09518","title":"Gradient Aligned Attacks via a Few Queries","created":"2022-05-19","cited_by_count":0,"area":null},{"id":"2206.00924","title":"FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples","created":"2022-06-02","cited_by_count":0,"area":null},{"id":"2210.17140","title":"Scoring Black-Box Models for Adversarial Robustness","created":"2022-10-31","cited_by_count":0,"area":null},{"id":"2305.13651","title":"Adversarial Defenses via Vector Quantization","created":"2023-05-23","cited_by_count":0,"area":null},{"id":"2310.03358","title":"Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria","created":"2023-10-05","cited_by_count":0,"area":null},{"id":"2401.13212","title":"AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation","created":"2024-01-24","cited_by_count":0,"area":null},{"id":"2406.15635","title":"DataFreeShield: Defending Adversarial Attacks without Training Data","created":"2024-06-21","cited_by_count":0,"area":null},{"id":"2407.08806","title":"HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks","created":"2024-07-11","cited_by_count":0,"area":null},{"id":"2407.13625","title":"Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls","created":"2024-07-18","cited_by_count":0,"area":null},{"id":"2408.03972","title":"Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks","created":"2024-08-07","cited_by_count":0,"area":null},{"id":"2501.10466","title":"Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reduction","created":"2025-01-15","cited_by_count":0,"area":null},{"id":"2502.21041","title":"Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing","created":"2025-02-28","cited_by_count":0,"area":null},{"id":"2506.08611","title":"Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2506.12733","title":"Learning to Fuse: Modality-Aware Adaptive Scheduling for Robust Multimodal Foundation Models","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2509.09960","title":"Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2510.10000","title":"Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2512.10275","title":"Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2605.09663","title":"Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation","created":"2026-05-10","cited_by_count":0,"area":null},{"id":"2606.22516","title":"The Scissors Effect: When Resize-Based Input Diversity Helps or Hurts Transfer Attacks","created":"2026-06-21","cited_by_count":0,"area":null},{"id":"2607.19855","title":"Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation","created":"2026-07-22","cited_by_count":0,"area":null}],"next_cursor":null}