{"total":21,"rows":[{"id":"2207.05382","title":"Frequency Domain Model Augmentation for Adversarial Attack","created":"2022-07-12","cited_by_count":130,"area":null},{"id":"2209.15179","title":"Physical Adversarial Attack meets Computer Vision: A Decade Survey","created":"2022-09-30","cited_by_count":67,"area":null},{"id":"2203.05154","title":"Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack","created":"2022-03-10","cited_by_count":44,"area":null},{"id":"2201.11528","title":"Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains","created":"2022-01-27","cited_by_count":42,"area":null},{"id":"2210.02041","title":"Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks","created":"2022-10-05","cited_by_count":38,"area":null},{"id":"2407.10179","title":"CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks","created":"2024-07-14","cited_by_count":19,"area":null},{"id":"2212.09035","title":"Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks","created":"2022-12-18","cited_by_count":14,"area":null},{"id":"2110.12734","title":"Fast Gradient Non-sign Methods","created":"2021-10-25","cited_by_count":9,"area":null},{"id":"2203.04607","title":"Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation","created":"2022-03-09","cited_by_count":9,"area":null},{"id":"2310.20175","title":"LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations","created":"2023-10-31","cited_by_count":8,"area":null},{"id":"2305.01361","title":"Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature","created":"2023-05-02","cited_by_count":6,"area":null},{"id":"2405.06340","title":"Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing","created":"2024-05-10","cited_by_count":3,"area":null},{"id":"2303.05719","title":"Boosting Adversarial Attacks by Leveraging Decision Boundary Information","created":"2023-03-10","cited_by_count":1,"area":null},{"id":"2502.02096","title":"Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimization","created":"2025-02-04","cited_by_count":1,"area":null},{"id":"2212.06431","title":"Object-fabrication Targeted Attack for Object Detection","created":"2022-12-13","cited_by_count":0,"area":null},{"id":"2412.14080","title":"On the Robustness of Distributed Machine Learning against Transfer Attacks","created":"2024-12-18","cited_by_count":0,"area":null},{"id":"2502.12207","title":"PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2504.14137","title":"Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach","created":"2025-04-19","cited_by_count":0,"area":null},{"id":"2505.20782","title":"Towards Cross-Domain Multi-Targeted Adversarial Attacks","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2602.23117","title":"Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2606.18860","title":"Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation","created":"2026-06-17","cited_by_count":0,"area":null}],"next_cursor":null}