{"total":47,"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":"2207.12391","title":"SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness","created":"2022-07-25","cited_by_count":74,"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":"2203.14046","title":"A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies","created":"2022-03-26","cited_by_count":44,"area":null},{"id":"2202.09844","title":"Sparsity Winning Twice: Better Robust Generalization from More Efficient Training","created":"2022-02-20","cited_by_count":41,"area":null},{"id":"2212.05630","title":"DISCO: Adversarial Defense with Local Implicit Functions","created":"2022-12-11","cited_by_count":34,"area":null},{"id":"2303.10653","title":"Randomized Adversarial Training via Taylor Expansion","created":"2023-03-19","cited_by_count":28,"area":null},{"id":"2204.00089","title":"Investigating Top-$k$ White-Box and Transferable Black-box Attack","created":"2022-03-30","cited_by_count":26,"area":null},{"id":"2212.04985","title":"Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization","created":"2022-12-09","cited_by_count":22,"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":"2308.12857","title":"Fast Adversarial Training with Smooth Convergence","created":"2023-08-24","cited_by_count":14,"area":null},{"id":"2111.10130","title":"Fooling Adversarial Training with Inducing Noise","created":"2021-11-19","cited_by_count":13,"area":null},{"id":"2203.01439","title":"Enhancing Adversarial Robustness for Deep Metric Learning","created":"2022-03-02","cited_by_count":10,"area":null},{"id":"2107.04401","title":"Improving Model Robustness with Latent Distribution Locally and Globally","created":"2021-07-08","cited_by_count":9,"area":null},{"id":"2106.03614","title":"Adversarial Attack and Defense in Deep Ranking","created":"2021-06-07","cited_by_count":8,"area":null},{"id":"2407.07557","title":"Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging","created":"2024-07-10","cited_by_count":8,"area":null},{"id":"2308.16454","title":"Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff","created":"2023-08-31","cited_by_count":7,"area":null},{"id":"2301.09069","title":"Provable Unrestricted Adversarial Training without Compromise with Generalizability","created":"2023-01-22","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":"2112.09219","title":"All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines","created":"2021-12-16","cited_by_count":5,"area":null},{"id":"2303.06302","title":"Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey","created":"2023-03-11","cited_by_count":5,"area":null},{"id":"2311.10992","title":"Towards Robust and Accurate Visual Prompting","created":"2023-11-18","cited_by_count":5,"area":null},{"id":"2102.00662","title":"Towards Speeding up Adversarial Training in Latent Spaces","created":"2021-02-01","cited_by_count":4,"area":null},{"id":"2110.13980","title":"Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions","created":"2021-10-26","cited_by_count":4,"area":null},{"id":"2211.16316","title":"A3T: Accuracy Aware Adversarial Training","created":"2022-11-29","cited_by_count":4,"area":null},{"id":"2309.04650","title":"Exploring Robust Features for Improving Adversarial Robustness","created":"2023-09-09","cited_by_count":4,"area":null},{"id":"2105.15157","title":"Adaptive Feature Alignment for Adversarial Training","created":"2021-05-31","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":"2111.05063","title":"Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search","created":"2021-11-09","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":"2101.08675","title":"Adversarial Machine Learning in Text Analysis and Generation","created":"2021-01-14","cited_by_count":2,"area":null},{"id":"2103.03076","title":"Dynamic Efficient Adversarial Training Guided by Gradient Magnitude","created":"2021-03-04","cited_by_count":2,"area":null},{"id":"2308.02753","title":"DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation","created":"2023-08-05","cited_by_count":2,"area":null},{"id":"2408.04077","title":"PushPull-Net: Inhibition-driven ResNet robust to image corruptions","created":"2024-08-07","cited_by_count":2,"area":null},{"id":"2105.09394","title":"Balancing Robustness and Sensitivity using Feature Contrastive Learning","created":"2021-05-19","cited_by_count":1,"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":"2108.11785","title":"A Hierarchical Assessment of Adversarial Severity","created":"2021-08-26","cited_by_count":1,"area":null},{"id":"2303.06241","title":"Do we need entire training data for adversarial training?","created":"2023-03-10","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":"2106.07428","title":"Audio Attacks and Defenses against AED Systems -- A Practical Study","created":"2021-06-14","cited_by_count":0,"area":null},{"id":"2108.10217","title":"Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks","created":"2021-08-23","cited_by_count":0,"area":null},{"id":"2112.00428","title":"Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation","created":"2021-12-01","cited_by_count":0,"area":null},{"id":"2112.13989","title":"Associative Adversarial Learning Based on Selective Attack","created":"2021-12-28","cited_by_count":0,"area":null},{"id":"2302.08066","title":"Masking and Mixing Adversarial Training","created":"2023-02-16","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":"2506.06823","title":"Exploring Visual Prompting: Robustness Inheritance and Beyond","created":"2025-06-07","cited_by_count":0,"area":null}],"next_cursor":null}