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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Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning
Nov 12, 2025Jian Wang, Hong Shen, Chan-Tong LamAdversarial Attacks Backdoor Attacks in Federated Learning
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China · School of Cyberspace Security, Guangzhou University of Software, Guangzhou, China · School of Engineering and Technology, Central Queensland University, Australia
Solver-Integrated Adversarial Attacking and Training of Neural Operators
Oct 21, 2025Yifei SunAdversarial Training Adversarial Attacks
UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
Oct 19, 2025Mansi Phute, Matthew Hull, Haoran Wang +6Adversarial Attacks Adversarial Perturbations
Georgia Institute of Technology, USA · Technology Innovation Institute, UAE
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Sep 26, 2025Li Xia, Jing Yu, Zheng Liu +3Gradient Inversion Attacks Adversarial Attacks
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Aug 27, 2025Viktor Valadi, Lucas Beerens, Mattias Åkesson +4Gradient Inversion Attacks Adversarial Attacks
Scaleout Systems Sweden · Recorded Future Sweden · AI Sweden Sweden +1
On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
Jun 14, 2025Matteo Vilucchio, Lenka Zdeborová, Bruno LoureiroAdversarial Attacks Adversarial Robustness
Information Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Département d’Informatique, École Normale Supérieure - PSL & CNRS, France
Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
May 26, 2025Shijie Liu, Andrew C. Cullen, Paul Montague +2Adversarial Attacks Backdoor Attacks
School of Computing and Information Systems, University of Melbourne, Parkville, Australia · DST Group, Adelaide
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
May 19, 2025Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh +2Adversarial Attacks Adversarial Examples
Department of Computer Science University of California, Irvine · Whiting School of Engineering Johns Hopkins University
How to Backdoor Image Knowledge Distillation
Apr 30, 2025Qian Ma, Chen Wu, Prasenjit Mitra +1Adversarial Attacks Data Poisoning Attacks
The Pennsylvania State University State College, USA · Carnegie Mellon University Africa Kigali, Rwanda
Boosting the Local Invariance for Better Adversarial Transferability
Mar 8, 2025Bohan Liu, Xiaosen WangAdversarial Attacks Transfer-Based Adversarial Attacks
School of Computer Science and Technology, Xidian University, Xi’an 710126, China · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Approaching the Harm of Gradient Attacks While Only Flipping Labels
Feb 28, 2025Abdessamad El-Kabid, El-Mahdi El-MhamdiFederated Learning Aggregation Adversarial Attacks
École Polytechnique
On Adversarial Attacks In Acoustic Drone Localization
Feb 27, 2025Tamir Shor, Chaim Baskin, Alex BronsteinAdversarial Attacks UAV Navigation
Department of Computer Science Technion – Israel Institute of Technology Haifa, Israel · School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be’er Sheva, Israel · Technion – Israel Institute of Technology +1
Towards Reliable Recommender Systems for Rating Data
Dec 30, 2024Aurore Archimbaud, Andreas Alfons, Ines WilmsAdversarial Attacks Recommender Systems
TBS Business School · Erasmus University Rotterdam · Maastricht University
GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning
Dec 17, 2024Rui Zhang, Ka-Ho ChowGradient Inversion Attacks Adversarial Attacks
HKU Musketeers Foundation Institute of Data Science · The University of Hong Kong · Hong Kong SAR, China +1
Stealthy Multi-Task Adversarial Attacks
Nov 26, 2024Jiacheng Guo, Tianyun Zhang, Lei Li +3Adversarial Attacks Adversarial Perturbations
Cleveland State University, Cleveland, OH 44115, USA · University of Wisconsin-Madison, Madison, WI 53706, USA · Western Digital Research, San Jose, CA 95119, USA
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Oct 2, 2024Sina Mavali, Jonas Ricker, David Pape +2Adversarial Attacks Adversarial Robustness
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Ruhr University Bochum, Bochum, Germany
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Jun 14, 2024Srishti Gupta, Zhang Chen, Luca Demetrio +9Adversarial Attacks Neural Network Robustness
Sapienza University of Rome, Rome, Italy. · University of Cagliari, Cagliari, Italy. · Northwestern Polytechnical University, Xi’An, China. +1
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
Feb 6, 2024Oubo Ma, Yuwen Pu, Linkang Du +5Adversarial Attacks Partially Observable RL
Zhejiang University · Xi’an Jiaotong University · Laboratory for Big Data and Decision +2
SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models
Dec 13, 2023Feifei Wang, Zhentao Tan, Tianyi Wei +2Adversarial Attacks T2I Diffusion Models
University of Science and Technology of China · Alibaba Cloud
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Oct 6, 2023Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4Byzantine-Robust Federated Learning Adversarial Attacks
University of California, Irvine · Texas A&M University · University of Birmingham +3
Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Date pendingKamel Kamel, Hridoy Sankar Dutta, Keshav Sood +1Audio Deepfake Detection Speaker Verification
School of Information Technology, Deakin University, Australia
Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability
Date pendingYudong Gao, Linghan Chen, Wenhan Wu +5Adversarial Attacks Adversarial Attacks on VLA Models
The Hong Kong University of Science and Technology · Adelaide University · Wuhan University +3

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Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning

Jian Wang, Hong Shen, Chan-Tong Lam

Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China · School of Cyberspace Security, Guangzhou University of Software, Guangzhou, China · School of Engineering and Technology, Central Queensland University, Australia

Adversarial Attacks Backdoor Attacks in Federated Learning Adversarial Perturbations Federated Learning

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Traditional distributed backdoor attacks (DBA) in federated learning improve stealthiness by decomposing global triggers into sub-triggers, which however requires more poisoned data to maintian the attck strength and hence increases the exposure risk. To overcome this defect, This paper proposes a novel method, namely Fractal-Triggerred Distributed Backdoor Attack (FTDBA), which leverages the self-similarity of fractals to enhance the feature strength of sub-triggers and hence significantly reduce the required poisoning volume for the same attack strength. To address the detectability of fractal structures in the frequency and gradient domains, we introduce a dynamic angular perturbation mechanism that adaptively adjusts perturbation intensity across the training phases to balance efficiency and stealthiness. Experiments show that FTDBA achieves a 92.3% attack success rate with only 62.4% of the poisoning volume required by traditional DBA methods, while reducing the detection rate by 22.8% and KL divergence by 41.2%. This study presents a low-exposure, high-efficiency paradigm for federated backdoor attacks and expands the application of fractal features in adversarial sample generation.

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