Anomaly Detection

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Overview

Anomaly detection studies how to identify observations or events that depart from expected behavior, often when examples of real anomalies are scarce or unknown. Common approaches learn normality through density estimation, isolation methods, reconstruction or forecasting, while newer methods use graph structure, multimodal features, synthetic anomalies, and pretrained vision or language models. Work spans settings such as industrial images, sensor time series, network traffic, and transaction graphs, with benchmarks including MVTec AD, SWaT, and DCASE used to assess detection and localization. A central challenge is balancing sensitivity against false alarms as data shift, training sets become contaminated, and models move from curated benchmarks to constrained real-world deployments.

Latest papers

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657 papers61 papers in the last four weeks, level with the four weeks before. 0.4% of all new papers.

  1. World Model Failure Classification and Anomaly Detection for Autonomous Inspection

    Feb 18, 2026Michelle Ho, Muhammad Fadhil Ginting, Isaac R. Ward +4Robot Failure DetectionRobotic Anomaly Detection

    Stanford University, Stanford, CA 94305 USA · Field AI, Mission Viejo, CA 92691 USA

  2. Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters

    Feb 18, 2026Diego Labate, Dipanwita Thakur, Giancarlo FortinoFederated LearningAnomaly Detection

    DIMES, University of Calabria, Rende, Italy

  3. Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

    Feb 18, 2026Srikumar Nayak, James WalmesleyCyber Threat IntelligenceFederated Graph Learning

    Incedo Inc., New Jersey, USA · Indian Institute of Technology, Chennai, Tamil Nadu, India · University of Kent, Kent, UK

  4. Benchmarking IoT Time-Series AD with Event-Level Augmentations

    Feb 17, 2026Dmitry Zhevnenko, Ilya Makarov, Aleksandr Kovalenko +6Time-Series Anomaly DetectionAnomaly Detection

    AXXX, Moscow, Russia · Research Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, Russia · Trusted AI Center, RAS, Moscow, Russia +1

  5. Triplet Feature Fusion for Equipment Anomaly Prediction : An Open-Source Methodology Using Small Foundation Models

    Feb 16, 2026Takato YasunoAnomaly DetectionPredictive Maintenance

  6. Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings

    Feb 10, 2026Laura Paul, Holger Rauhut, Martin Burger +2Crack SegmentationImage Inverse Problems

    Dept. of Mathematics, LMU Munich, Germany · Munich Center for Machine Learning, Germany · Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, Germany +2

  7. Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?

    Feb 10, 2026Donghwan Kim, Junghun Phee, Hyunsoo YoonAnomaly DetectionTabular Anomaly Detection

    Department of Industrial Engineering, University of Yonsei, Republic of Korea, Seoul · H. Milton Stewart School of Industrial and Systems Engineering (ISyE), Georgia Institute of Technology, USA, GA

  8. When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents

    Feb 9, 2026Yuting Ning, Jaylen Jones, Zhehao Zhang +5LLM Self-CorrectionComputer-Use Agents

    The Ohio State University · Amazon AGI

  9. Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol Effects

    Jan 16, 2026Jose Sánchez AndreuMahalanobis DistanceIndustrial Anomaly Detection

    Independent Researcher, Murcia, Spain

  10. A Hybrid Insider Threat Detection Framework Combining Multi-Agent Simulation, Layered SIEM Correlation, and Theory-of-Mind Reasoning

    Jan 6, 2026Firdous Kausar, Asmah Muallem, Naw Safrin Sattar +1CybersecurityAnomaly Detection

    Department of Computer Science and Data Science SACS, Meharry Medical College Nashville, USA

  11. Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT

    Jan 5, 2026Mohammed Ayalew Belay, Adil Rasheed, Pierluigi Salvo RossiCommunication-Efficient Distributed TrainingIndustrial Anomaly Detection

    Department of Electronic Systems, Norwegian University of Science and Technology, 7034 Trondheim, Norway

  12. Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

    Dec 17, 2025Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li +1TransformerIntelligent Transportation Systems

    Networked Systems Security (NSS) Group, KTH Royal Institute of Technology, Stockholm, Sweden · Tech Validation – AI department at the Chief Technology Officer (CTO) Organization, Lenovo, Beijing, China

  13. PA-VAD: Diffusion-Based Pseudo-Only Video Anomaly Detection via Domain-Aligned Memory Updates

    Dec 7, 2025Satoshi Hashimoto, Yanan Wang, Hitoshi Nishimura +1Video Anomaly DetectionSynthetic Anomaly Generation

    KDDI Research, Inc., Fujimino, Saitama, Japan

  14. Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies

    Nov 5, 2025Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel +3Kernel MethodsSemi-Supervised Anomaly Detection

    NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, 02139, MA · Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, 02139, MA · School of Engineering and Applied Sciences, Harvard University, Allston, 02134, MA +1

  15. Seeing is believing: The case for hardware-centric inefficiency detection and remediation

    Oct 29, 2025Ziji Chen, Steven W. D. Chien, Peng Qian +1Cloud ComputingRoot Cause Analysis

    University of Oxford

  16. A Novel GPT-Based Framework for Anomaly Detection in System Logs

    Oct 16, 2025Zeng Zhang, Wenjie Yin, Xiaoqi LiFocal LossAnomaly Detection

    Hainan University Haikou, China

  17. Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Models

    Oct 15, 2025Akib Mohammed Khan, Bartosz KrawczykFew-Shot Anomaly DetectionAnomaly Detection

    Rochester Institute of Technology Rochester, USA

  18. Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection

    Oct 15, 2025Yang Cao, Sikun Yang, Hao Tian +5Tabular Anomaly DetectionIsolation Forest

    Tsinghua Shenzhen International Graduate School, Tsinghua University, China · School of Computing and Information Technology, Great Bay University, China · School of Compuer Science, Nanjing University, China +3

  19. Adversarial Stress Testing of Outlier Detection in Subjective Image Quality Assessment

    Sep 8, 2025Dietmar SaupeAnomaly DetectionImage Quality Assessment

    Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

  20. Can Multi-modal (reasoning) LLMs detect document manipulation?

    Aug 14, 2025Zisheng Liang, Kidus Zewde, Rudra Pratap Singh +7Multimodal Large Language ModelsMLLM Evaluation

    Duke University · Indian Institute of Technology, Roorkee · New York University +3

  21. AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization

    Aug 6, 2025Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6VLM AdaptationIndustrial Anomaly Detection

    1Nanyang Technological University · Institute for Infocomm Research (I2R), A*STAR · 3South China University of Technology

  22. Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs

    Mar 25, 2025Vinayak Mali, Saurabh JaiswalFall DetectionHuman Activity Recognition

    Department of Artificial Intelligence Indian Institue of Technology Kharagpur

  23. FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning

    Feb 23, 2025Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad SaifData Poisoning AttacksFederated Learning

    Department of Electrical and Computer Engineering, University of Windsor, Windsor ON, Canada · Faculty of Computer Science, University of New Brunswick, Fredericton NB, Canada

  24. AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

    Feb 13, 2025Hezhe Qiao, Chaoxi Niu, Ling Chen +1Graph-Based Anomaly DetectionAnomaly Detection

    School of Computing and Information Systems Singapore Management University Singapore · Australian Artificial Intelligence Institute University of Technology Sydney Sydney, Australia

  25. Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners

    Dec 12, 2024Xinyue Liu, Jianyuan Wang, Biao Leng +1Cross-Modal Knowledge DistillationIndustrial Anomaly Detection

    School of Computer Science and Engineering, Beihang University, Beijing 100191, China · Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, and the School of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China · School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China

  26. Monotonic anomaly detection

    Oct 30, 2024Oliver Urs Lenz, Matthijs van LeeuwenIsolation ForestSemi-Supervised Anomaly Detection

    Department of Statistics, Data Science and Modelling, National Institute for Public Health and the Environment, The Netherlands · Leiden Institute of Advanced Computer Science, Leiden University · Department of Applied Mathematics, Computer Science and Statistics, Ghent University

  27. Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

    May 29, 2024Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1Weakly Supervised LearningSemi-Supervised Anomaly Detection

    NTT