{"total":22,"rows":[{"id":"2103.01498","title":"A Survey On Universal Adversarial Attack","created":"2021-03-02","cited_by_count":59,"area":null},{"id":"2110.02797","title":"Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs","created":"2021-10-06","cited_by_count":57,"area":null},{"id":"2203.04412","title":"ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches","created":"2022-03-07","cited_by_count":27,"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":"2207.10899","title":"Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness","created":"2022-07-22","cited_by_count":20,"area":null},{"id":"2102.06479","title":"Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective","created":"2021-02-12","cited_by_count":18,"area":null},{"id":"2104.03000","title":"Universal Adversarial Training with Class-Wise Perturbations","created":"2021-04-07","cited_by_count":13,"area":null},{"id":"2211.15030","title":"Imperceptible Adversarial Attack via Invertible Neural Networks","created":"2022-11-28","cited_by_count":12,"area":null},{"id":"2211.01671","title":"Visual Adversarial Attacks and Defenses in the Physical World: A Survey","created":"2022-11-03","cited_by_count":11,"area":null},{"id":"2104.01732","title":"Semantically Stealthy Adversarial Attacks against Segmentation Models","created":"2021-04-05","cited_by_count":7,"area":null},{"id":"2310.12431","title":"SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation","created":"2023-10-19","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":"2204.03154","title":"Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines","created":"2022-04-07","cited_by_count":2,"area":null},{"id":"2206.10858","title":"Robust Universal Adversarial Perturbations","created":"2022-06-22","cited_by_count":2,"area":null},{"id":"2212.07495","title":"SAIF: Sparse Adversarial and Imperceptible Attack Framework","created":"2022-12-14","cited_by_count":2,"area":null},{"id":"2411.10500","title":"Edge-Only Universal Adversarial Attacks in Distributed Learning","created":"2024-11-15","cited_by_count":2,"area":null},{"id":"2112.15329","title":"On Distinctive Properties of Universal Perturbations","created":"2021-12-31","cited_by_count":1,"area":null},{"id":"2202.11197","title":"Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks","created":"2022-02-20","cited_by_count":1,"area":null},{"id":"2208.06228","title":"Unifying Gradients to Improve Real-world Robustness for Deep Networks","created":"2022-08-12","cited_by_count":1,"area":null},{"id":"2310.13019","title":"Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm","created":"2023-10-18","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":"2107.04749","title":"Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations","created":"2021-07-10","cited_by_count":0,"area":null}],"next_cursor":null}