{"total":49,"rows":[{"id":"2103.14641","title":"On Generating Transferable Targeted Perturbations","created":"2021-03-26","cited_by_count":65,"area":null},{"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":"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":"2210.04213","title":"Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective","created":"2022-10-09","cited_by_count":37,"area":null},{"id":"2307.12280","title":"Downstream-agnostic Adversarial Examples","created":"2023-07-23","cited_by_count":32,"area":null},{"id":"2409.17874","title":"DarkSAM: Fooling Segment Anything Model to Segment Nothing","created":"2024-09-26","cited_by_count":32,"area":null},{"id":"2111.09961","title":"A Review of Adversarial Attack and Defense for Classification Methods","created":"2021-11-18","cited_by_count":29,"area":null},{"id":"2110.01823","title":"Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations","created":"2021-10-05","cited_by_count":28,"area":null},{"id":"2110.03605","title":"Robust Feature-Level Adversaries are Interpretability Tools","created":"2021-10-07","cited_by_count":27,"area":null},{"id":"2205.11981","title":"OPOM: Customized Invisible Cloak towards Face Privacy Protection","created":"2022-05-24","cited_by_count":22,"area":null},{"id":"2308.06015","title":"Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation","created":"2023-08-11","cited_by_count":22,"area":null},{"id":"2111.04266","title":"Generative Dynamic Patch Attack","created":"2021-11-08","cited_by_count":19,"area":null},{"id":"2310.00633","title":"A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks","created":"2023-10-01","cited_by_count":19,"area":null},{"id":"2112.06116","title":"Stereoscopic Universal Perturbations across Different Architectures and Datasets","created":"2021-12-12","cited_by_count":17,"area":null},{"id":"2104.03356","title":"Universal Spectral Adversarial Attacks for Deformable Shapes","created":"2021-04-07","cited_by_count":14,"area":null},{"id":"2110.12321","title":"ADC: Adversarial attacks against object Detection that evade Context consistency checks","created":"2021-10-24","cited_by_count":12,"area":null},{"id":"2105.02480","title":"A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking","created":"2021-05-06","cited_by_count":9,"area":null},{"id":"2106.08746","title":"Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses","created":"2021-06-16","cited_by_count":9,"area":null},{"id":"2406.06089","title":"Texture Re-scalable Universal Adversarial Perturbation","created":"2024-06-10","cited_by_count":9,"area":null},{"id":"2105.07553","title":"Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing","created":"2021-05-17","cited_by_count":8,"area":null},{"id":"2104.01732","title":"Semantically Stealthy Adversarial Attacks against Segmentation Models","created":"2021-04-05","cited_by_count":7,"area":null},{"id":"2102.06747","title":"Realizable Universal Adversarial Perturbations for Malware","created":"2021-02-12","cited_by_count":5,"area":null},{"id":"2209.09883","title":"Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks","created":"2022-09-20","cited_by_count":5,"area":null},{"id":"2308.07673","title":"A Review of Adversarial Attacks in Computer Vision","created":"2023-08-15","cited_by_count":5,"area":null},{"id":"2209.13113","title":"FG-UAP: Feature-Gathering Universal Adversarial Perturbation","created":"2022-09-27","cited_by_count":4,"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":"2508.05989","title":"ETA: Energy-based Test-time Adaptation for Depth Completion","created":"2025-08-08","cited_by_count":4,"area":null},{"id":"2303.10594","title":"AdaptGuard: Defending Against Universal Attacks for Model Adaptation","created":"2023-03-19","cited_by_count":3,"area":null},{"id":"2402.16397","title":"Investigating Deep Watermark Security: An Adversarial Transferability Perspective","created":"2024-02-26","cited_by_count":3,"area":null},{"id":"2502.21048","title":"Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior","created":"2025-02-28","cited_by_count":3,"area":null},{"id":"2102.00029","title":"You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries","created":"2021-01-29","cited_by_count":2,"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":"2307.14751","title":"FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks","created":"2023-07-27","cited_by_count":2,"area":null},{"id":"2406.05535","title":"Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability","created":"2024-06-08","cited_by_count":2,"area":null},{"id":"2408.01715","title":"Joint Universal Adversarial Perturbations with Interpretations","created":"2024-08-03","cited_by_count":2,"area":null},{"id":"2105.15057","title":"Dominant Patterns: Critical Features Hidden in Deep Neural Networks","created":"2021-05-31","cited_by_count":1,"area":null},{"id":"2110.03124","title":"Improving Adversarial Robustness for Free with Snapshot Ensemble","created":"2021-10-07","cited_by_count":1,"area":null},{"id":"2112.15329","title":"On Distinctive Properties of Universal Perturbations","created":"2021-12-31","cited_by_count":1,"area":null},{"id":"2206.05664","title":"An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines","created":"2022-06-12","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":"2502.05542","title":"Democratic Training Against Universal Adversarial Perturbations","created":"2025-02-08","cited_by_count":1,"area":null},{"id":"2108.04409","title":"On Procedural Adversarial Noise Attack And Defense","created":"2021-08-10","cited_by_count":0,"area":null},{"id":"2211.09959","title":"Potential Auto-driving Threat: Universal Rain-removal Attack","created":"2022-11-18","cited_by_count":0,"area":null},{"id":"2303.04980","title":"Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation","created":"2023-03-09","cited_by_count":0,"area":null},{"id":"2304.03955","title":"Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack","created":"2023-04-08","cited_by_count":0,"area":null},{"id":"2408.00193","title":"Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges","created":"2024-07-31","cited_by_count":0,"area":null},{"id":"2503.22205","title":"Data-Free Universal Attack by Exploiting the Intrinsic Vulnerability of Deep Models","created":"2025-03-28","cited_by_count":0,"area":null}],"next_cursor":null}