Adversarial Attacks

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28 papers in the last four weeks, up 155% on the four weeks before. 0.3% of all new papers.

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Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision
Sep 21, 2026Florian Krone, Elena Hoemann, Sven HallerbachAdversarial Attacks Adversarial Robustness
Institute for AI Safety and Security German Aerospace Center (DLR) Sankt Augustin, Germany
StyleAT: Defending Face Recognition Against Semantic Attacks
Sep 20, 2026Ben Shapira, Roi Cohen, Shang-Tse Chen +1Adversarial Training Adversarial Attacks
Tel Aviv University Tel Aviv, Israel · HPI / University of Potsdam Potsdam, Germany · National Taiwan University Taipei, Taiwan
Investigating Adversarial Robustness of Heterogeneous Cooperative Perception
Sep 15, 2026Chenyi Wang, Yutong Liu, Qingzhao Zhang +1Adversarial Attacks Adversarial Robustness
School of Electrical, Computing, and Software Engineering University of Arizona Tucson, AZ 85721 USA
Implementing a White-Box Undetectable Backdoor for Random Fourier Features
Sep 14, 2026Michael Collins, Jada Cumberland, Brianne Dunn +3Adversarial Attacks Backdoor Attacks
Laboratory for Advanced Cybersecurity Research Laurel, MD 20707 · School of Cybersecurity Old Dominion University Norfolk, VA 23259 · Old Dominion University Norfolk, VA 23259 +1
Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks
Sep 14, 2026Adam Haroon, Erick J. Rodríguez-Seda, Tristan Schuler +1RL Control Reinforcement Learning
On Identifying Adversarial Intent Injection in AI-Native 6G Networks
Sep 14, 2026Nilesh Chakraborty, Petar Djukic, Burak Kantarci6G Networks Adversarial Attacks
University of Ottawa, Ottawa, ON, Canada · Nokia Bell Labs, 600 March Road, Kanata, ON K2K 2E6, Canada
Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
Sep 10, 2026Toshif Khan, Muhammad AbusaqerAdversarial Attacks Backdoor Attacks
Minot State University · Department of Math, Data & Technology Minot State University Minot, ND, 58707 · Trustworthy Language Intelligence Lab, Minot State University
Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending
Sep 9, 2026Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei +1Adversarial Training Adversarial Attacks
University of Twente, Faculty of Behavioural, Management and Social Sciences, Department of High-Tech Business and Entrepreneurship, AE Enschede, 7500, Netherlands
Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks
Sep 9, 2026Yaning Jia, Shenyang Deng, Yaoqing Yang +3Graph Neural Networks Adversarial Attacks
Department of Computer Science Dartmouth College Hanover, New Hampshire, USA
An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems
Sep 9, 2026Quoc Viet Nguyen, Trinh Pham, Viet Huynh +4Adversarial Attacks Recommender Systems
Griffith University, Australia · Edith Cowan University, Australia · The University of Queensland, Australia +1
Optimizing Byzantine Node Placement in Decentralized Federated Learning
Sep 1, 2026Edoardo Gabrielli, Gabriele TolomeiByzantine-Robust Federated Learning Adversarial Attacks
Dipartimento di Ingegneria Informatica, Automatica e Gestionale Sapienza University of Rome Rome, Italy · Dipartimento di Informatica Sapienza University of Rome Rome, Italy
Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain
Sep 1, 2026Daizong Liu, Junhao Dong, Zhiyuan Ma +6Adversarial Attacks on VLMs Adversarial Attacks
Institute for Math & AI, Wuhan University, Wuhan, 430072, China · College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China +3
Does Reasoning Mitigate Backdoor Attacks? A Neuro-Symbolic Perspective
Aug 31, 2026Marco Antonio Corallo, Andrea Agiollo, Mauro Conti +1Adversarial Attacks Adversarial Robustness
Örebro University · Örebro, Sweden · Delft University of Technology +3
Workload Identification with Physical Side Channels for AI Governance
Aug 31, 2026Simone Gargiulo, Gabriel KulpAdversarial Attacks AI Governance
Pivotal Research · Intelligence Security Laboratories
OASIS: Optimizing Attacker Sequences for Hard-Label Black-Box Text Attacks
Aug 30, 2026Qian Chen, Shiliang Xiao, Yuzhi LiangAdversarial Attacks Black-Box Adversarial Attacks
School of Information Science and Technology Guangdong University of Foreign Studies, China
GENADA: efficient generative time series adversarial attack framework
Aug 12, 2026Michael Baronov, Denis Vorobev, Margarita Rusanova +2Adversarial Attacks Adversarial Perturbations
Moscow Independent Research Institute of Artificial Intelligence · HSE University
Refusing Everything Looks Safe: Restoring the Benign Arm to Encoded-Prompt Evaluation
Aug 12, 2026Haoyu Zhang, Haowen Xu, Xiao Luo +7LLM Safety Benchmarks Language Model Safety Evaluation
Northeastern University
Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models
Aug 11, 2026Jiahui Han, Yuhui Yao, Xin Wang +6Adversarial Attacks Adversarial Attacks on VLA Models
1Xi’an Jiaotong University · 2Shanghai AI Laboratory · University of Science and Technology of China
Beyond Decision Boundaries: Relational Geometry Attacks on Contrastive Embedding Manifolds
Aug 10, 2026Fei Zhao, Peiyuan Zhang, Xi Li +2Contrastive Learning Neural Representation Geometry
The University of Alabama at Birmingham, Birmingham, USA · Texas A&M University, College Station, USA
Stealing Reasoning Traces from Proprietary LLM APIs
Aug 10, 2026Alexander Panfilov, David Schmotz, Ilia Shumailov +5Model Extraction Attacks Adversarial Attacks
MATS Research · ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems +4
Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
Aug 10, 2026Panav Shah, Avishek GhoshAdversarial Attacks Data Poisoning Attacks
Indian Institute of Technology, Bombay Mumbai, India
IDATA: Scalable Invertible Diffusion for Unrestricted Adversarial Transfer Attack
Aug 9, 2026Yi Pan, Jun-Jie Huang, Tianrui Liu +3Adversarial Attacks Transfer-Based Adversarial Attacks
College of Computer Science and Technology
Query-Only Backdoor Attacks on Self-Evolving Skills via Trajectory Poisoning
Aug 8, 2026Yuyang Luo, Haoran Wang, Kai ShuLLM Backdoor Attacks Adversarial Attacks
1Emory University
Quality-Diversity Stress Tests for Process Reward Models:What Archive Coverage Can and Cannot Certify
Aug 8, 2026Ibne Farabi Shihab, Fariya AfrinProcess Reward Models Adversarial Attacks
Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology
Adversarial Attacks on Deep OCR Systems
Aug 7, 2026Wenbo Sun, Hongzong LI, Yanyun Wang +5Adversarial Attacks on VLMs Adversarial Attacks
Nanjing University of Aeronautics and Astronautics · Northwestern Polytechnical University · The Chinese University of Hong Kong +4
Casting the Net! Revisiting MasterFace Impersonation Attacks
Aug 7, 2026Seunghun Paik, Sunpill Kim, Chanwoo Hwang +1Biometric Identification Adversarial Attacks
Hanyang University Seoul, Republic of Korea
AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles
Aug 7, 2026Jinlei Wang, Jiahuan Long, Mingkai Sun +9Adversarial Attacks Physical Adversarial Attacks
Sun Yat-sen University · 2Chinese Academy of Military Science · 3Shenzhen University +2
Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers
Aug 7, 2026Ridma Jayasundara, Shaheer Mohamed, Tharindu Fernando +6Adversarial Attacks Adversarial Robustness
Signal Processing, Artificial Intelligence and Vision Technologies Group (SAIVT), Queensland University of Technology · Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · School of Computing, National University of Singapore +1
Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning
Aug 6, 2026Srinivasan Subramanian, Md. Abdullah Al Hafiz Khan, Kazi Aminul IslamFederated Learning Aggregation Byzantine-Robust Federated Learning
Department of Computer Science Kennesaw State University Marietta, USA
When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems
Aug 6, 2026Jialuo Chen, Lingqi Jiang, Xinhao Deng +7Adversarial Attacks AI Agent Security
Ant Group · Zhejiang University · Tsinghua University +3

Sep 21, 2026, cs.LGJ/K move · Enter open · S save

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

Florian Krone, Elena Hoemann, Sven Hallerbach

Institute for AI Safety and Security German Aerospace Center (DLR) Sankt Augustin, Germany

Adversarial Attacks Adversarial Robustness Adversarial Perturbations Black-Box Adversarial Attacks

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Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small perturbations, almost imperceptible to humans, which significantly alter the model's prediction. These adversarial attacks are often considered to be a significant threat to the implementation of neural networks in safety-critical applications. Most attacks utilize the white-box threat model and therefore require full access to the target model, making them unrealistic to use in practice. We propose a novel approach under the more realistic black-box threat model that utilizes concepts from reinforcement learning to optimize perturbations with a non-differentiable target model. Reinforcement learning algorithms have already been optimized to be query efficient, making them an ideal starting point when designing black-box adversarial attacks. We show the success of our reinforcement learning inspired black-box adversarial attack (RIBA) in generating adversarial perturbations using only a small number of queries to the target model, by comparing it to state of the art attacks on different models on the Cifar10 and ImageNet data sets. RIBA takes

25.4\%

fewer median queries to generate attacked images against a ResNet-18 on Cifar10 and

22.5\%

fewer median queries to fool a Vit-B/16 model on ImageNet. Additionally, we demonstrate that RIBA can match the performance of white-box attacks on an adversarially trained model.

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