Adversarial Patch Attacks

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 25

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TAPDreamer: Transferable Adversarial Patches for World Action Models
Oct 5, 2026Xuanyu Lu, Fengqing Jiang, Kaiyuan Zheng +8Adversarial Patch Attacks Adversarial Attacks
University of Washington · University of Georgia · Western Washington University +2
Printability-Constrained Adversarial Decals for Near-Nadir Aerial Perception: Measured Ink Gamuts, Nested Realism Constraints, and a Physical-World Bound
Sep 27, 2026Sandesh Shrestha, K. T. Yasas Mahima, Asanka G. PereraAdversarial Patch Attacks Adversarial Attacks
Industrial Systems Engineering (ISE), Asian Institute of Technology (AIT), Pathum Thani 12120, Thailand
Beyond Patch Removal: Persistent Adversarial Effects in Vision-Language-Action Policies
Sep 17, 2026Enhao Wu, Fusen Guo, Yuxin Cao +3Adversarial Patch Attacks Adversarial Attacks on VLA Models
UNSW Sydney, Australia · UNSW Canberra, Australia · National University of Singapore, Singapore +2
AgentHijack: Visual Patch Attacks on Multimodal Computer-Use Agents
Sep 10, 2026Zhihao Liu, Hongyu Sun, Zhiyuan Fu +7Adversarial Patch Attacks Computer-Use Agents
Hainan University, Hainan 570228, China · University of Chinese Academy of Sciences National Computer Network Intrusion Protection Center, Beijing 101408, China · School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models
Sep 2, 2026Chengyin Hu, Dingyi Lu, Jiaju Han +5Adversarial Patch Attacks Large Vision-Language Models
Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling
Aug 31, 2026Xiaopei Zhu, Siyuan Huang, Zhanhao Hu +3Thermal Imaging Adversarial Patch Attacks
Department of Computer Science and Technology, Institute for Artificial Intelligence, BNRist, Tsinghua University, Beijing 100084, China · Chinese Institute for Brain Research (CIBR), Beijing 100010, China
A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization
Aug 5, 2026Anadi Goyal, Nandish Chattopadhyay, Anupam Chattopadhyay +1Adversarial Patch Attacks Adversarial Attacks
Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India · Nanyang Technological University, 50 Nanyang Ave, Singapore 639798
ColorFD: A Finite-Difference Guided Black-Box Physical Adversarial Attack for Remote Sensing Object Detection
Aug 5, 2026Tiannuo Guo, Guhang Qiu, Yuzhen Xie +3Remote Sensing Image Understanding Adversarial Patch Attacks
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
DRIFT: Derailing Denoising Trajectories of Flow-Matching VLAs with Adversarial Patch Attack
Aug 4, 2026Hoseong Tae, Jong-Seok LeeAdversarial Patch Attacks Adversarial Attacks on VLA Models
School of Integrated Technology, Yonsei University
VLAGuard: A Framework for Evaluating and Mitigating Physical Attention Hijacking in Vision-Language-Action Robots within Wireless Sensor Networks
Aug 2, 2026Dongfu Yin, Jinquan ZhangAdversarial Patch Attacks Adversarial Attacks on VLA Models
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Shenzhen University, Shenzhen, China
Hiding in Plain Sight: An Effective Physical Adversarial Patch Attack against Visual-Infrared Fused Face Detection
Jul 25, 2026Qiucheng Yu, Tao Ni, Yihe Zhou +2Adversarial Patch Attacks Face Recognition
City University of Hong Kong · King Abdullah University of Science and Technology (KAUST)
AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression
Jul 19, 2026Yuanhao Huang, Yilong Ren, Jinlei Wang +3Adversarial Patch Attacks Adversarial Attacks
School of Transportation Science and Engineering, Beihang University, 100191, Beijing, P.R China · Zhongguancun Laboratory, 100191, Beijing, P.R China · School of Systems Science and Engineering, Sun Yat-Sen University, 510006, Guangzhou, P.R China +1
Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
Jul 8, 2026Giulia Marchiori Pietrosanti, Giulio Rossolini, Giorgio ButtazzoAdversarial Patch Attacks Adversarial Attacks
Department of Excellence in Robotics and AI, Sant’Anna School of Advanced Study, Italy
InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models
Jul 8, 2026Xin Li, Jiaju Han, Ma Yaqi +5VLM Robustness Adversarial Patch Attacks
China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, China · Shenzhen Research Institute of Big Data, Shenzhen, China · Guizhou University, Guiyang, China +1
Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection
Jun 16, 2026Pedram MohajerAnsari, Amir Salarpour, David Fernandez +1Adversarial Patch Attacks Object Detection
School of Computing, Clemson University, USA
Structured Adversarial Camouflage via Voronoi Diagrams
Jun 16, 2026Jens Bayer, Stefan Becker, David Münch +2Adversarial Patch Attacks Object Detection
Fraunhofer IOSB and Fraunhofer Center for Machine Learning · Karlsruhe Institute of Technology
Detecting Aimbot Cheaters in MOGs
Jun 2, 2026Salman Shaikh, Tao Ni, Marc DacierAdversarial Patch Attacks Adversarial Examples
Partially Observable Adversarial Patch Attacks on Vision-Language-Action Models in Robotics
Jun 2, 2026Xiaofei Wang, Mingliang Han, Tianyu Hao +3VLM Robustness Adversarial Patch Attacks
Department of Automation, University of Science and Technology of China, Hefei, China · SmartMore Corporation · Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China +2
Digital-to-Physical Transfer of Adversarial Patches for Aerial Vehicle Detection
May 29, 2026Jung Heum Woo, Eun-Kyu LeeAdversarial Patch Attacks Adversarial Robustness
School of Information Technology Incheon National University Incheon 22012, Republic of Korea
AdvScene: Rethinking Adversarial Patch Evaluation Through Scene Robustness
May 28, 2026Xiaoyong, Yuan, Lan +1Adversarial Patch Attacks Adversarial Robustness
Brian · Clemson University · Clemson, SC, USA +1
VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
May 27, 2026Jiyuan Fu, Kaixun Jiang, Jingkai Jia +7Adversarial Patch Attacks Adversarial Attacks on VLA Models
Fudan University · The Hong Kong Polytechnic University
Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability
May 21, 2026Xiang Chen, Yuxian Dong, Chao Li +6VLM Robustness Adversarial Patch Attacks
The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches
Apr 29, 2026Vishesh Kumar, Akshay AgarwalAdversarial Patch Attacks OOD Detection
Trustworthy BiometraVision Lab, Indian Institute of Science Education and Research Bhopal, India
Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving
Apr 25, 2026Zihui Zhu, Ziqi Zhou, Yichen Wang +3Adversarial Patch Attacks Autonomous Driving Perception
Huazhong University of Science and Technology
Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models
Apr 24, 2026Shihui Yan, Ziqi Zhou, Yufei Song +3Adversarial Patch Attacks Transfer-Based Adversarial Attacks
Huazhong University of Science and Technology China

Oct 5, 2026, cs.CVJ/K move · Enter open · S save

TAPDreamer: Transferable Adversarial Patches for World Action Models

Xuanyu Lu, Fengqing Jiang, Kaiyuan Zheng, Yichen Feng+7

University of Washington · University of Georgia · Western Washington University+2

Adversarial Patch Attacks Adversarial Attacks World Action Models Transfer-Based Adversarial Attacks

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World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet world action models depend on camera inputs whose manipulation can corrupt the visual representations used across tasks and action policies. Existing attacks on these models optimize against the victim's actions or predicted futures and therefore require access to target-model outputs. In this paper, we propose an attack, TAPDreamer, against world action models that instead uses a public encoder alone to construct a fixed local perturbation that transfers across tasks and action architectures. TAPDreamer requires no target-policy queries. Our key insight is that interactions between patch-induced changes in attention weights and value vectors broadcast a nearly identical representation shift far beyond the patch footprint, and this shift remains stable across task observations. Guided by this insight, TAPDreamer uses six frames from one source task to maximize the global L1 distance between clean and patched encoder representations. In closed-loop evaluation, one frozen patch per benchmark, covering about 6.5% of the input, reduces FastWAM's success rate from 97.7% to 0.0% across 40 LIBERO tasks and from 90.86% to 0.0% across 50 RoboTwin tasks; matched random patches retain 81.5% and 79.2% success. The same patches reduce success to 1.45% and 1.00% on two DreamWAM configurations and to 10.60% on Motus. These results show that protecting downstream action generation alone is insufficient: defenses for world action models must also secure shared visual encoders against persistent local perturbations.

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