{"total":72,"rows":[{"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":"2203.03818","title":"Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon","created":"2022-03-08","cited_by_count":101,"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":"2108.09034","title":"AdvDrop: Adversarial Attack to DNNs by Dropping Information","created":"2021-08-20","cited_by_count":56,"area":null},{"id":"2108.06247","title":"Optical Adversarial Attack","created":"2021-08-13","cited_by_count":43,"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":"2205.05909","title":"Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World","created":"2022-05-12","cited_by_count":42,"area":null},{"id":"2207.08859","title":"Prior-Guided Adversarial Initialization for Fast Adversarial Training","created":"2022-07-18","cited_by_count":35,"area":null},{"id":"2502.13175","title":"Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks","created":"2025-02-18","cited_by_count":34,"area":null},{"id":"2212.05709","title":"HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design","created":"2022-12-12","cited_by_count":27,"area":null},{"id":"2209.14262","title":"A Survey on Physical Adversarial Attack in Computer Vision","created":"2022-09-28","cited_by_count":25,"area":null},{"id":"2205.13412","title":"Physical-World Optical Adversarial Attacks on 3D Face Recognition","created":"2022-05-26","cited_by_count":23,"area":null},{"id":"2307.07653","title":"RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World","created":"2023-07-14","cited_by_count":23,"area":null},{"id":"2204.06213","title":"Defensive Patches for Robust Recognition in the Physical World","created":"2022-04-13","cited_by_count":20,"area":null},{"id":"2308.06173","title":"Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook","created":"2023-08-11","cited_by_count":20,"area":null},{"id":"2301.13487","title":"Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks","created":"2023-01-31","cited_by_count":17,"area":null},{"id":"2209.04779","title":"Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense","created":"2022-09-11","cited_by_count":14,"area":null},{"id":"2401.03582","title":"Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception","created":"2024-01-07","cited_by_count":14,"area":null},{"id":"2407.12443","title":"Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective","created":"2024-07-17","cited_by_count":13,"area":null},{"id":"2412.16958","title":"Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature","created":"2024-12-22","cited_by_count":13,"area":null},{"id":"2103.13813","title":"RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy","created":"2021-03-22","cited_by_count":11,"area":null},{"id":"2206.01034","title":"Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs","created":"2022-06-02","cited_by_count":11,"area":null},{"id":"2209.02430","title":"Adversarial Color Film: Effective Physical-World Attack to DNNs","created":"2022-09-02","cited_by_count":11,"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":"2304.10712","title":"Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World","created":"2023-04-21","cited_by_count":11,"area":null},{"id":"2311.01473","title":"Adversarial Examples in the Physical World: A Survey","created":"2023-11-01","cited_by_count":10,"area":null},{"id":"2312.14217","title":"Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World","created":"2023-12-21","cited_by_count":10,"area":null},{"id":"2303.12249","title":"State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems","created":"2023-03-22","cited_by_count":8,"area":null},{"id":"2310.15444","title":"Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks","created":"2023-10-24","cited_by_count":8,"area":null},{"id":"2404.00897","title":"Machine Learning Robustness: A Primer","created":"2024-04-01","cited_by_count":8,"area":null},{"id":"2302.14267","title":"Adversarial Attack with Raindrops","created":"2023-02-28","cited_by_count":7,"area":null},{"id":"2204.00853","title":"Adversarial Neon Beam: A Light-based Physical Attack to DNNs","created":"2022-04-02","cited_by_count":6,"area":null},{"id":"2409.09860","title":"Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective","created":"2024-09-15","cited_by_count":6,"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":"2306.12111","title":"A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking","created":"2023-06-21","cited_by_count":5,"area":null},{"id":"2406.05857","title":"Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks","created":"2024-06-09","cited_by_count":5,"area":null},{"id":"2203.13479","title":"Enhancing Transferability of Adversarial Examples with Spatial Momentum","created":"2022-03-25","cited_by_count":4,"area":null},{"id":"2206.12251","title":"Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs","created":"2022-06-23","cited_by_count":4,"area":null},{"id":"2309.09480","title":"Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization","created":"2023-09-18","cited_by_count":4,"area":null},{"id":"2312.09554","title":"Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks","created":"2023-12-15","cited_by_count":4,"area":null},{"id":"2409.04133","title":"Secure Traffic Sign Recognition: An Attention-Enabled Universal Image Inpainting Mechanism against Light Patch Attacks","created":"2024-09-06","cited_by_count":4,"area":null},{"id":"2402.15919","title":"Learning to See Through Dazzle","created":"2024-02-24","cited_by_count":3,"area":null},{"id":"2404.00924","title":"BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks","created":"2024-04-01","cited_by_count":3,"area":null},{"id":"2409.00029","title":"Attack Anything: Blind DNNs via Universal Background Adversarial Attack","created":"2024-08-17","cited_by_count":3,"area":null},{"id":"2409.12394","title":"The Fluorescent Veil: A Stealthy and Effective Physical Adversarial Patch Against Traffic Sign Recognition","created":"2024-09-19","cited_by_count":3,"area":null},{"id":"2412.13709","title":"Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems","created":"2024-12-18","cited_by_count":3,"area":null},{"id":"2207.02963","title":"The Weaknesses of Adversarial Camouflage in Overhead Imagery","created":"2022-07-06","cited_by_count":2,"area":null},{"id":"2303.17890","title":"Fooling Polarization-based Vision using Locally Controllable Polarizing Projection","created":"2023-03-31","cited_by_count":2,"area":null},{"id":"2403.10330","title":"Towards Non-Adversarial Algorithmic Recourse","created":"2024-03-15","cited_by_count":2,"area":null},{"id":"2407.08514","title":"Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems","created":"2024-07-11","cited_by_count":2,"area":null},{"id":"2209.09652","title":"Adversarial Color Projection: A Projector-based Physical Attack to DNNs","created":"2022-09-19","cited_by_count":1,"area":null},{"id":"2209.11739","title":"Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs","created":"2022-09-19","cited_by_count":1,"area":null},{"id":"2310.09792","title":"SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack","created":"2023-10-15","cited_by_count":1,"area":null},{"id":"2311.12857","title":"Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2312.03085","title":"ScAR: Scaling Adversarial Robustness for LiDAR Object Detection","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2408.06625","title":"DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real World","created":"2024-08-13","cited_by_count":1,"area":null},{"id":"2503.00384","title":"A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges","created":"2025-03-01","cited_by_count":1,"area":null},{"id":"2504.08414","title":"Adversarial Examples in Environment Perception for Automated Driving (Review)","created":"2025-04-11","cited_by_count":1,"area":null},{"id":"2107.11986","title":"Benign Adversarial Attack: Tricking Models for Goodness","created":"2021-07-26","cited_by_count":0,"area":null},{"id":"2311.15994","title":"Adversarial Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights","created":"2023-11-27","cited_by_count":0,"area":null},{"id":"2312.06163","title":"Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors","created":"2023-12-11","cited_by_count":0,"area":null},{"id":"2403.03674","title":"Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors","created":"2024-03-06","cited_by_count":0,"area":null},{"id":"2408.12727","title":"BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks","created":"2024-08-22","cited_by_count":0,"area":null},{"id":"2501.08258","title":"Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World","created":"2025-01-14","cited_by_count":0,"area":null},{"id":"2501.14005","title":"Device-aware Optical Adversarial Attack for a Portable Projector-camera System","created":"2025-01-23","cited_by_count":0,"area":null},{"id":"2503.21164","title":"Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2508.13907","title":"Learning to See Through Flare","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2601.00270","title":"Rectifying Adversarial Examples Using Their Vulnerabilities","created":"2026-01-01","cited_by_count":0,"area":null},{"id":"2606.14504","title":"Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attacks","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2607.24194","title":"Face Age Verification Vulnerabilities Under Simple Appearance Manipulations","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2607.26651","title":"Physically Real-time Infrared Attack against Optical Flow Estimation Networks","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.30839","title":"Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling","created":"2026-08-31","cited_by_count":0,"area":null}],"next_cursor":null}