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Adversarial Sticker: A Stealthy Attack Method in the Physical World
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-58548-8_1openalex-snapshot
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
inferredcites →https://doi.org/10.1007/978-3-030-58558-7_39openalex-snapshot
Adversarial T-Shirt! Evading Person Detectors in a Physical World
inferredcites →https://doi.org/10.1007/978-3-030-58592-1_29openalex-snapshot
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search
inferredcites →https://doi.org/10.1007/978-3-319-92627-8_9openalex-snapshot
Introduction to Face Presentation Attack Detection
inferredcites →https://doi.org/10.1109/cvpr.2015.7298682openalex-snapshot
FaceNet: A unified embedding for face recognition and clustering
inferredcites →https://doi.org/10.1109/cvpr.2016.124openalex-snapshot
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
inferredcites →https://doi.org/10.1109/cvpr.2016.232openalex-snapshot
Traffic-Sign Detection and Classification in the Wild
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.713openalex-snapshot
SphereFace: Deep Hypersphere Embedding for Face Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00175openalex-snapshot
Robust Physical-World Attacks on Deep Learning Visual Classification
inferredcites →https://doi.org/10.1109/cvpr.2018.00552openalex-snapshot
CosFace: Large Margin Cosine Loss for Deep Face Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/cvpr.2019.00790openalex-snapshot
Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00108openalex-snapshot
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00334openalex-snapshot
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01498openalex-snapshot
The Translucent Patch: A Physical and Universal Attack on Object Detectors
inferredcites →https://doi.org/10.1109/cvprw.2019.00012openalex-snapshot
Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00415openalex-snapshot
Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study
inferredcites →https://doi.org/10.1109/icpr48806.2021.9412236openalex-snapshot
AdvHat: Real-World Adversarial Attack on ArcFace Face ID System
inferredcites →https://doi.org/10.1109/sibircon48586.2019.8958122openalex-snapshot
Real-world Attack on MTCNN Face Detection System
inferredcites →https://doi.org/10.1109/sibircon48586.2019.8958134openalex-snapshot
On Adversarial Patches: Real-World Attack on ArcFace-100 Face Recognition System
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot
One Pixel Attack for Fooling Deep Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2798607openalex-snapshot
Multimodal Machine Learning: A Survey and Taxonomy
inferredcites →https://doi.org/10.1109/tpami.2020.3005434openalex-snapshot
Deep Learning for 3D Point Clouds: A Survey
inferredcites →https://doi.org/10.1109/tpami.2020.3032061openalex-snapshot
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning
inferredcites →https://doi.org/10.1109/tpami.2020.3033291openalex-snapshot
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
inferredcites →https://doi.org/10.1109/tpami.2021.3059968openalex-snapshot
Image Segmentation Using Deep Learning: A Survey
inferredcites →https://doi.org/10.1145/311535.311556openalex-snapshot
A morphable model for the synthesis of 3D faces
inferredcites →https://doi.org/10.1145/3317611openalex-snapshot
A General Framework for Adversarial Examples with Objectives
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
inferredcites →https://doi.org/10.3390/jimaging6120139openalex-snapshot
A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices
inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot
Delving into Transferable Adversarial Examples and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1712.08062openalex-snapshot
Note on Attacking Object Detectors with Adversarial Stickers
inferredcites →https://doi.org/10.48550/arxiv.1802.06430openalex-snapshot
DARTS: Deceiving Autonomous Cars with Toxic Signs
inferredcites →https://doi.org/10.48550/arxiv.1802.09707openalex-snapshot
Understanding and Enhancing the Transferability of Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1803.04683openalex-snapshot
Invisible Mask: Practical Attacks on Face Recognition with Infrared
inferredcites →https://doi.org/10.48550/arxiv.1806.02299openalex-snapshot
DPatch: An Adversarial Patch Attack on Object Detectors
inferredcites →https://doi.org/10.48550/arxiv.2010.14974openalex-snapshot
Object Hider: Adversarial Patch Attack Against Object Detectors
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Adversarial Infrared Blocks: A Multi-View Black-Box Attack to Thermal Infrared Detectors in Physical World
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Short Paper: Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception
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Research on Adversarial Patterns in Apparel with Digital Environments
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Sticking Information in Plain Sight: Encoding and Detecting Hidden Stickers in the Real World
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Defending Adversarial Patches via Joint Region Localizing and Inpainting
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How to Defend and Secure Deep Learning Models Against Adversarial Attacks in Computer Vision: A Systematic Review
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Enhancing fast adversarial training: precision-aware initialization with historical perturbation guidance
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Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective
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Hiding from infrared detectors in real world with adversarial clothes
inferred← citeshttps://doi.org/10.1007/s11263-023-01963-yopenalex-snapshot
Infrared Adversarial Patches with Learnable Shapes and Locations in the Physical World
inferred← citeshttps://doi.org/10.1007/s11263-024-02120-9openalex-snapshot
Towards Robust Semantic Segmentation against Patch-Based Attack via Attention Refinement
inferred← citeshttps://doi.org/10.1007/s11263-025-02467-7openalex-snapshot
Defending Against Adversarial Examples Via Modeling Adversarial Noise
inferred← citeshttps://doi.org/10.1016/b978-0-44-323761-4.00012-2openalex-snapshot
Machine learning robustness: a primer
inferred← citeshttps://doi.org/10.1016/j.asoc.2023.110777openalex-snapshot
MISPSO-Attack: An efficient adversarial watermarking attack based on multiple initial solution particle swarm optimization
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.112818openalex-snapshot
Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world
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Adversarial Neon Beam: A light-based physical attack to DNNs
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State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems
inferred← citeshttps://doi.org/10.1016/j.imavis.2023.104861openalex-snapshot
Adversarial color projection: A projector-based physical-world attack to DNNs
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112576openalex-snapshot
Efficient physical image attacks using adversarial fast autoaugmentation methods
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114395openalex-snapshot
PADetBench: Towards benchmarking texture- and patch-based physical attacks against object detection
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EAP: An effective black-box impersonation adversarial patch attack method on face recognition in the physical world
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Adversarial infrared blocks: A multi-view black-box attack to thermal infrared detectors in physical world
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Adversarial Infrared Curves: An attack on infrared pedestrian detectors in the physical world
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Mitigating low-frequency bias: Feature recalibration and frequency attention regularization for adversarial robustness
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ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches
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Attention‐guided evolutionary attack with elastic‐net regularization on face recognition
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Generative attack in complex real-world scenarios
inferred← citeshttps://doi.org/10.1049/cvi2.12264openalex-snapshot
Adversarial catoptric light: An effective, stealthy and robust physical‐world attack to DNNs
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From Pixel to Peril: Investigating Adversarial Attacks on Aerial Imagery Through Comprehensive Review and Prospective Trajectories
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Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
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MIAD-MARK: Adversarial Watermarking of Medical Image for Protecting Copyright and Privacy
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Energy-Efficient Federated Learning for Space Observation via Neuromorphic Computing: a Use Case
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Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey
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Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey
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Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.01187openalex-snapshot
Physically Adversarial Infrared Patches with Learnable Shapes and Locations
inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.02314openalex-snapshot
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
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ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector
inferred← citeshttps://doi.org/10.1109/dsn-w65791.2025.00071openalex-snapshot
Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10889387openalex-snapshot
Post-Hoc Adversarial Stickers Against Micro-Expression Leakage
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A Brief Review of Anti-Face Recognition
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Unified Adversarial Patch for Cross-modal Attacks in the Physical World
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RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
inferred← citeshttps://doi.org/10.1109/iceccme57830.2023.10252727openalex-snapshot
Adversarial Attacks on Traffic Sign Recognition: A Survey
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A Black-box based Attack Generation Approach to Create the Transferable Patch Attack
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MysticMask: Adversarial Mask for Impersonation Attack Against Face Recognition Systems
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Characterizing Physical Adversarial Attacks on Robot Motion Planners
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Contextual Adversarial Attack Against Aerial Detection in The Physical World
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Dual Ensemble Enhanced Physical Attacks on Aerial Detection
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Adaptive Adversarial Patch Attack on Face Recognition Models
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A Physical Attack for Segmentation-Based Visual Foundation Models
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Hierarchical ML for Adversarial Resiliency in Autonomous Vehicular Traffic Sign Recognition
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Model Agnostic Defense against Adversarial Patch Attacks on Object Detection in Unmanned Aerial Vehicles
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Multi-Scenario Simulation of Machine Learning Based Vision Attacks in CARLA
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Adversarial Attacks against Traffic Sign Recognition: Taxonomy and Open Research Areas
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Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors
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Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors
inferred← citeshttps://doi.org/10.1109/jiot.2024.3519599openalex-snapshot
Adversarial Lens Flares: A Threat to Camera-Based Systems in Smart Devices
inferred← citeshttps://doi.org/10.1109/jiot.2025.3574668openalex-snapshot
Universal Closed-Box Adversarial Attack for Trajectory Representation via Controlling High-Dimensional Iterative Constraints
inferred← citeshttps://doi.org/10.1109/jiot.2025.3581207openalex-snapshot
B²PATCH: Designing Adversarial Patches to Manipulate Bounding Box Perception in Vehicular Attacks
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Adversarial Patches for Fooling Positive–Negative Obstacle Detectors on Unstructured Roads
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A Survey on Adversarial Deep Learning in Computer Vision
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BadLogo: A Physically Realizable Adversarial Sticker for Evaluating the Robustness of Face Recognition Models
inferred← citeshttps://doi.org/10.1109/sp46215.2023.10179429openalex-snapshot
DepthFake: Spoofing 3D Face Authentication with a 2D Photo
inferred← citeshttps://doi.org/10.1109/tbiom.2024.3417372openalex-snapshot
Toward Deep Face Spoofing: Taxonomy, Recent Advances, and Open Challenges
inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3307150openalex-snapshot
Cross-Shaped Adversarial Patch Attack
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3540881openalex-snapshot
Universal Multi-View Black-Box Attack Against Object Detectors via Layout Optimization
inferred← citeshttps://doi.org/10.1109/tdsc.2024.3445570openalex-snapshot
Multi-Modal Spoofing Attacks on 3D Face Liveness Detection via a Single 2D Photo
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3603988openalex-snapshot
No Trespassing: Ground-View Adversarial Patches for Privacy-Aware Management in COTS Robot Vacuum Cleaner
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3626196openalex-snapshot
NarAdv: Natural-Style Physical Adversarial Attack on Traffic Sign Detection for Autonomous Vehicles
inferred← citeshttps://doi.org/10.1109/tdsc.2026.3674133openalex-snapshot
Proactive Defense for Physical-World 3D Adversarial Face Presentation Attacks
inferred← citeshttps://doi.org/10.1109/tencon66050.2025.11375519openalex-snapshot
Attack-SH: Adversarial Attacks on Self-Healing Material Properties Prediction Model
inferred← citeshttps://doi.org/10.1109/tgrs.2022.3225306openalex-snapshot
Benchmarking Adversarial Patch Against Aerial Detection
inferred← citeshttps://doi.org/10.1109/tgrs.2023.3264839openalex-snapshot
CBA: Contextual Background Attack Against Optical Aerial Detection in the Physical World
inferred← citeshttps://doi.org/10.1109/tgrs.2024.3386533openalex-snapshot
Fooling Aerial Detectors by Background Attack via Dual-Adversarial-Induced Error Identification
inferred← citeshttps://doi.org/10.1109/tgrs.2024.3397354openalex-snapshot
DeMPAA: Deployable Multi-Mini-Patch Adversarial Attack for Remote Sensing Image Classification
inferred← citeshttps://doi.org/10.1109/tgrs.2024.3426272openalex-snapshot
Physical Adversarial Attacks Against Aerial Object Detection With Feature-Aligned Expandable Textures
inferred← citeshttps://doi.org/10.1109/tgrs.2025.3559027openalex-snapshot
Cloud Adversarial Example Generation for Remote Sensing Image Classification
inferred← citeshttps://doi.org/10.1109/tgrs.2026.3736804openalex-snapshot
BCAP: Black-Box Cross-Modal Adversarial Patch Attacks Against Multimodal Vehicle Recognition Systems via UAV Remote Sensing
inferred← citeshttps://doi.org/10.1109/tifs.2024.3402167openalex-snapshot
Toward Transferable Attack via Adversarial Diffusion in Face Recognition
inferred← citeshttps://doi.org/10.1109/tifs.2024.3516577openalex-snapshot
Physical Adversarial Patch Attack for Optical Fine-Grained Aircraft Recognition
inferred← citeshttps://doi.org/10.1109/tifs.2025.3620155openalex-snapshot
PoisonPatch: Natural Adversarial Patches via Diffusion Models and Federated Learning Poisoning
inferred← citeshttps://doi.org/10.1109/tifs.2025.3643157openalex-snapshot
IlluAttack: Generating Adversarial Examples via Illumination Transformation for Face Anti-Spoofing
inferred← citeshttps://doi.org/10.1109/tip.2023.3276331openalex-snapshot
On Single-Model Transferable Targeted Attacks: A Closer Look at Decision-Level Optimization
inferred← citeshttps://doi.org/10.1109/tiv.2024.3403667openalex-snapshot
Securing Autonomous Vehicles Visual Perception: Adversarial Patch Attack and Defense Schemes With Experimental Validations
inferred← citeshttps://doi.org/10.1109/tmm.2023.3315550openalex-snapshot
Boosting Adversarial Transferability With Learnable Patch-Wise Masks
inferred← citeshttps://doi.org/10.1109/tmm.2026.3651134openalex-snapshot
Boosting Universal Adversarial Attack on Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/tpami.2022.3231886openalex-snapshot
Simultaneously Optimizing Perturbations and Positions for Black-Box Adversarial Patch Attacks
inferred← citeshttps://doi.org/10.1109/tpami.2023.3262592openalex-snapshot
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos
inferred← citeshttps://doi.org/10.1109/tpami.2023.3330769openalex-snapshot
Unified Adversarial Patch for Visible-Infrared Cross-Modal Attacks in the Physical World
inferred← citeshttps://doi.org/10.1109/tpami.2024.3370721openalex-snapshot
Source-Free Domain Adaptation With Domain Generalized Pretraining for Face Anti-Spoofing
inferred← citeshttps://doi.org/10.1109/tpami.2024.3411035openalex-snapshot
Revisiting the Trade-Off Between Accuracy and Robustness via Weight Distribution of Filters
inferred← citeshttps://doi.org/10.1109/tpami.2024.3416308openalex-snapshot
Mitigating Accuracy-Robustness Trade-Off via Balanced Multi-Teacher Adversarial Distillation
inferred← citeshttps://doi.org/10.1109/tpami.2024.3416710openalex-snapshot
Sequential Manipulation Against Rank Aggregation: Theory and Algorithm
inferred← citeshttps://doi.org/10.1109/tpami.2024.3430860openalex-snapshot
Physical Adversarial Attack Meets Computer Vision: A Decade Survey
inferred← citeshttps://doi.org/10.1109/tpami.2024.3522994openalex-snapshot
Natural Adversarial Mask for Face Identity Protection in Physical World
inferred← citeshttps://doi.org/10.1109/tpami.2025.3526188openalex-snapshot
Distributionally Location-Aware Transferable Adversarial Patches for Facial Images
inferred← citeshttps://doi.org/10.1109/tpami.2025.3540200openalex-snapshot
Improving Adversarial Training From the Perspective of Class-Flipping Distribution
inferred← citeshttps://doi.org/10.1109/tpami.2025.3582334openalex-snapshot
Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling
inferred← citeshttps://doi.org/10.1109/tpami.2025.3596462openalex-snapshot
Real-World Adversarial Defense Against Patch Attacks Based on Diffusion Model
inferred← citeshttps://doi.org/10.1109/tpami.2026.3664842openalex-snapshot
AdvDiffusion: Adversarial Patches Generation for Face Recognition With High Transferability in Physical Domain
inferred← citeshttps://doi.org/10.1109/tpami.2026.3718681openalex-snapshot
Face De-Identification: A Domain-Centric Survey From Capture to Processing
inferred← citeshttps://doi.org/10.1109/tr.2025.3642046openalex-snapshot
HiCert: Toward Patch Robustness Certification and Detection for Deep Learning Systems Beyond Consistent Samples
inferred← citeshttps://doi.org/10.1109/wacv61041.2025.00643openalex-snapshot
DiffPAD: Denoising Diffusion-Based Adversarial Patch Decontamination
inferred← citeshttps://doi.org/10.1111/itor.70049openalex-snapshot
A cybersecurity risk analysis framework for systems with artificial intelligence components
inferred← citeshttps://doi.org/10.1145/3570361.3613278openalex-snapshot
Screen Perturbation: Adversarial Attack and Defense on Under-Screen Camera
inferred← citeshttps://doi.org/10.1145/3573428.3573621openalex-snapshot
A Small Sticker is Enough: Spoofing Face Recognition Systems via Small Stickers
inferred← citeshttps://doi.org/10.1145/3658644.3670382openalex-snapshot
The Invisible Polyjuice Potion: an Effective Physical Adversarial Attack against Face Recognition
inferred← citeshttps://doi.org/10.1145/3664647.3680659openalex-snapshot
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks
inferred← citeshttps://doi.org/10.1145/3665451.3665530openalex-snapshot
Signals Are All You Need: Detecting and Mitigating Digital and Real-World Adversarial Patches Using Signal-Based Features
inferred← citeshttps://doi.org/10.1145/3731715.3733425openalex-snapshot
SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models
inferred← citeshttps://doi.org/10.14722/ndss.2024.24920openalex-snapshot
DorPatch: Distributed and Occlusion-Robust Adversarial Patch to Evade Certifiable Defenses
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2459893/v1openalex-snapshot
A Survey on Physical Adversarial Attack in Computer Vision
inferred← citeshttps://doi.org/10.3390/app15126891openalex-snapshot
Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
inferred← citeshttps://doi.org/10.3390/jimaging11010025openalex-snapshot
A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects
inferred← citeshttps://doi.org/10.3390/jimaging11010026openalex-snapshot
Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization
inferred← citeshttps://doi.org/10.3390/math13040591openalex-snapshot
URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection
inferred← citeshttps://doi.org/10.3390/rs16142570openalex-snapshot
DBI-Attack:Dynamic Bi-Level Integrated Attack for Intensive Multi-Scale UAV Object Detection
inferred← citeshttps://doi.org/10.34133/remotesensing.0219openalex-snapshot
A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
inferred← citeshttps://doi.org/10.55164/ajstr.v28i1.254093openalex-snapshot
Enhancing Autonomous Driving: A Novel Approach of Mixed Attack and Physical Defense Strategies
inferred← citeshttps://doi.org/10.70675/38cb9544z5106z4ec9zaf28z6fedd9c361dcopenalex-snapshot
Security and Robustness of Autonomous Driving Systems Against Physical Adversarial Attack
inferred← citeshttps://doi.org/10.70675/690e25b2z1fb9z4d7fza6cbzcb09e2b773cfopenalex-snapshot
Neural networks security under realistic scenario
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