{"total":68,"rows":[{"id":"2302.14301","title":"A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking","created":"2023-02-28","cited_by_count":85,"area":null},{"id":"2303.01870","title":"Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models","created":"2023-03-03","cited_by_count":79,"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":"2308.16258","title":"Robust Principles: Architectural Design Principles for Adversarially Robust CNNs","created":"2023-08-30","cited_by_count":46,"area":null},{"id":"2203.09678","title":"Self-Ensemble Adversarial Training for Improved Robustness","created":"2022-03-18","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":"2311.13091","title":"Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise","created":"2023-11-22","cited_by_count":25,"area":null},{"id":"2209.14105","title":"Exploring the Relationship between Architecture and Adversarially Robust Generalization","created":"2022-09-28","cited_by_count":23,"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":"2209.07263","title":"Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)","created":"2022-09-15","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":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2302.01629","title":"Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels","created":"2023-02-03","cited_by_count":13,"area":null},{"id":"2302.12480","title":"Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?","created":"2023-02-24","cited_by_count":13,"area":null},{"id":"2206.09868","title":"Understanding Robust Learning through the Lens of Representation Similarities","created":"2022-06-20","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":"2203.01439","title":"Enhancing Adversarial Robustness for Deep Metric Learning","created":"2022-03-02","cited_by_count":10,"area":null},{"id":"2205.11156","title":"Squeeze Training for Adversarial Robustness","created":"2022-05-23","cited_by_count":10,"area":null},{"id":"2305.19377","title":"Benign Overfitting in Deep Neural Networks under Lazy Training","created":"2023-05-30","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":"2401.12236","title":"The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness","created":"2024-01-19","cited_by_count":9,"area":null},{"id":"2308.06703","title":"Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods","created":"2023-08-13","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":"2301.03110","title":"RobArch: Designing Robust Architectures against Adversarial Attacks","created":"2023-01-08","cited_by_count":6,"area":null},{"id":"2401.14948","title":"Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2403.13134","title":"Robust NAS under adversarial training: benchmark, theory, and beyond","created":"2024-03-19","cited_by_count":6,"area":null},{"id":"2210.05118","title":"Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization","created":"2022-10-11","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":"2207.05321","title":"Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures","created":"2022-07-12","cited_by_count":3,"area":null},{"id":"2309.13245","title":"RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias","created":"2023-09-23","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":"2405.05502","title":"Towards Accurate and Robust Architectures via Neural Architecture Search","created":"2024-05-09","cited_by_count":3,"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":"2212.14049","title":"Differentiable Search of Accurate and Robust Architectures","created":"2022-12-28","cited_by_count":2,"area":null},{"id":"2312.01260","title":"Rethinking PGD Attack: Is Sign Function Necessary?","created":"2023-12-03","cited_by_count":2,"area":null},{"id":"2406.10090","title":"Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis","created":"2024-06-14","cited_by_count":2,"area":null},{"id":"2210.13235","title":"Chaos Theory and Adversarial Robustness","created":"2022-10-20","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":"2401.14031","title":"Sparse and Transferable Universal Singular Vectors Attack","created":"2024-01-25","cited_by_count":1,"area":null},{"id":"2405.20672","title":"Investigating and unmasking feature-level vulnerabilities of CNNs to adversarial perturbations","created":"2024-05-31","cited_by_count":1,"area":null},{"id":"2406.14073","title":"Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2408.11680","title":"First line of defense: A robust first layer mitigates adversarial attacks","created":"2024-08-21","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":"2509.19197","title":"A Validation Strategy for Deep Learning Models: Evaluating and Enhancing Robustness","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2510.19761","title":"Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems","created":"2025-10-22","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":"2202.12435","title":"Understanding Adversarial Robustness from Feature Maps of Convolutional Layers","created":"2022-02-25","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":"2205.10617","title":"Gradient Concealment: Free Lunch for Defending Adversarial Attacks","created":"2022-05-21","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":"2402.04325","title":"Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2404.06313","title":"On adversarial training and the 1 Nearest Neighbor classifier","created":"2024-04-09","cited_by_count":0,"area":null},{"id":"2406.06792","title":"Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness","created":"2024-06-10","cited_by_count":0,"area":null},{"id":"2408.00794","title":"CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution","created":"2024-07-18","cited_by_count":0,"area":null},{"id":"2409.04819","title":"Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation","created":"2024-09-07","cited_by_count":0,"area":null},{"id":"2504.00038","title":"Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better","created":"2025-03-30","cited_by_count":0,"area":null},{"id":"2506.05032","title":"Identifying and Understanding Cross-Class Features in Adversarial Training","created":"2025-06-05","cited_by_count":0,"area":null},{"id":"2509.20793","title":"FERD: Fairness-Enhanced Data-Free Robustness Distillation","created":"2025-09-25","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":"2603.25517","title":"NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2605.18666","title":"A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.14865","title":"GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2606.31110","title":"Explaining Machine Learning and Memorization with Statistical Mechanics","created":"2026-06-30","cited_by_count":0,"area":null}],"next_cursor":null}