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. Unsupervised Symbolic Anomaly Detection

    Mar 18, 2026Md Maruf Hossain, Tim Katzke, Simon Klüttermann +1Unsupervised Anomaly DetectionAnomaly Detection

    TU Dortmund University, Dortmund, Germany · Research Center Trustworthy Data Science and Security, UA Ruhr, Germany

  2. AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection

    Mar 18, 2026Manuel Barusco, Davide Dalle Pezze, Francesco Borsatti +1Anomaly Detection

    University of Padova, Italy

  3. An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation

    Mar 17, 2026Kushankur Ghosh, Mehar Klair, Kian Kyars +2Data ProvenanceAnomaly Detection

    University of Alberta

  4. BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection

    Mar 17, 2026Melissa Schween, Mathis Kruse, Bodo RosenhahnAnomaly DetectionGraph-Based Anomaly Detection

    Institute for Information Processing, L3S - Leibniz University Hannover

  5. TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection

    Mar 17, 2026Pietro Bonazzi, Rafael Sutter, Luigi Capogrosso +2Anomaly DetectionIndustrial Anomaly Detection

    ETH Zurich, Zurich, Switzerland · Interdisciplinary Transformation University of Austria, Linz, Austria · Swiss Engineering Partners AG, Zurich, Switzerland

  6. Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models

    Mar 16, 2026Lexiang Xiong, Qi Li, Jingwen Ye +1Hallucination Detection in VLMsVLM Interpretability

    National University of Singapore · Monash University

  7. Multi-turn Physics-informed Vision-language Model for Physics-grounded Anomaly Detection

    Mar 16, 2026Yao Gu, Xiaohao Xu, Yingna WuReasoning for VLMsVideo Anomaly Detection

    Shanghaitech University · University of Michigan, Ann Arbor

  8. Towards One-for-All Anomaly Detection for Tabular Data

    Mar 15, 2026Shiyuan Li, Yixin Liu, Yu Zheng +3Tabular Anomaly DetectionAnomaly Detection

    Griffith University, Gold Coast, Australia · Tongji University, Shanghai, China

  9. Graph-Based Deep Learning for Intelligent Detection of Energy Losses, Theft, and Operational Inefficiencies in Oil & Gas Production Networks

    Mar 15, 2026AbdulQoyum A. Olowookere, Adewale U. Oguntola, Ebenezer. Leke OdekanleIndustrial Anomaly DetectionAnomaly Detection

    Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University, Ibadan, Oyo State, Nigeria. · Department of Computer Science, Abiola Ajimobi Technical University, Ibadan, Oyo State, Nigeria

  10. TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference

    Mar 15, 2026Patryk Marszałek, Tomasz Kuśmierczyk, Marek ŚmiejaTabular Anomaly DetectionAnomaly Detection

    Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland

  11. VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery

    Mar 14, 2026Fabrizio Genilotti, Arianna Stropeni, Francesco Borsatti +3Anomaly Detection

    University of Padova, Italy

  12. VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction

    Mar 14, 2026Hiroto Nakata, Yawen Zou, Shunsuke Sakai +5Anomaly DetectionIndustrial Anomaly Detection

    Department of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-city, 910-8507, Japan · Graduate School of Science and Engineering, University of Toyama, 3190 Gofuku, Toyama-city, 930-8555, Japan · Faculty of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-city, 910-8507, Japan +1

  13. GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systems

    Mar 14, 2026Yiling Tao, Xinran Zheng, Shuo Yang +2Graph-Based Anomaly DetectionLLM Agent Security

    Shenzhen International Graduate School, Tsinghua University · Department of Electrical and Electronic Engineering, The University of Hong Kong · School of Computer Science and Engineering, University of Electronic Science and Technology of China

  14. MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection

    Mar 14, 2026Lizhao Zhang, Shengsong Kong, Tao Guo +2AutoencodersEfficient Neural Network Inference

    Faculty of computing, Harbin Institute of Technology, Harbin 150000, China.

  15. Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling

    Mar 14, 2026Dingding Cao, Bianbian Jiao, Jingzong Yang +2Financial Fraud DetectionDecentralized Finance

    School of Big Data, Baoshan University, Baoshan 678000, Yunnan, China · School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, Shandong, China

  16. Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection

    Mar 14, 2026Phurich Saengthong, Takahiro ShinozakiAnomaly DetectionUnsupervised Anomaly Detection

    Department of Information and Communications Engineering, Institute of Science Tokyo, Japan

  17. Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE

    Mar 13, 2026Holger R. Roth, Sarthak Tickoo, Mayank Kumar +18Financial Fraud DetectionAnomaly Detection

    NVIDIA, Santa Clara, USA · J.P. Morgan, New York, USA · DeepTempo, San Francisco, USA +2

  18. MIRAGE: Model-agnostic Industrial Realistic Anomaly Generation and Evaluation for Visual Anomaly Detection

    Mar 13, 2026Jinwei Hu, Francesco Borsatti, Arianna Stropeni +3Synthetic Anomaly GenerationIndustrial Anomaly Detection

    University of Padova, Italy

  19. GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration

    Mar 13, 2026Yihao Ding, Yiran Zhang, Chris Gonzalez +2Unsupervised Anomaly DetectionAnomaly Detection

    The University of Western Australia, Perth, WA, Australia

  20. Topo-R1: Detecting Topological Anomalies via Vision-Language Models

    Mar 13, 2026Meilong Xu, Qingqiao Hu, Xiaoling Hu +6Anomaly DetectionVLM Evaluation

    Stony Brook University · Massachusetts General Hospital and Harvard Medical School · Stanford University +1

  21. Vision-Language Based Expert Reporting for Painting Authentication and Defect Detection

    Mar 13, 2026Eman Ouda, Mohammed Salah, Arsenii O. Chulkov +4Thermal ImagingAnomaly Detection

    Khalifa University of Science and Technology, Department of Aerospace Engineering, Abu Dhabi, United Arab Emirates · National Research Tomsk Polytechnic University, Russia · Academy of Fine Arts of Naples, Naples, Italy +3

  22. Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers

    Mar 13, 2026Yiqun Zhang, Zexi Tan, Xiaopeng Luo +1Graph-Based Anomaly DetectionAnomaly Detection

    School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

  23. ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation

    Mar 11, 2026Kadir-Kaan Özer, René Ebeling, Markus EnzweilerEfficient Neural Network InferenceAutomated Evaluation

    Mercedes-Benz AG, Germany. · Esslingen University of Applied Sciences, Germany.

  24. RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation

    Mar 11, 2026Shijie Zhou, Bin Zhu, Jiarui Yang +3Robotic Anomaly DetectionAnomaly Detection

    Institute of Trustworthy Embodied AI, Fudan University · Shanghai Key Laboratory of Multimodal Embodied AI · Singapore Management University

  25. Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregation

    Mar 11, 2026Yalun Qi, Sichen Zhao, Zhiming Xue +2Anomaly DetectionTime-Series Anomaly Detection

    Khoury College of Computer Science Northeastern University Boston United States · College of Engineering Northeastern University Boston United States · College of Professional Studies Northeastern University Boston United States

  26. TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection

    Mar 10, 2026Xiong Zhang, Hong Peng, Changlong Fu +3Anomaly DetectionCross-Dataset Generalization

    School of Software and AI, Yunnan University, Kunming, China

  27. The Boiling Frog Threshold: Criticality and Blindness in World Model-Based Anomaly Detection Under Gradual Drift

    Mar 9, 2026Zhe HongAI Agent MonitoringAnomaly Detection

    National University of Singapore

  28. Detecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method

    Mar 9, 2026Jing Zhang, Ke Huang, Yao Zhang +2Dynamic Graph LearningAnomaly Detection

    School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710600, China · School of Computer Science, Northwestern Polytechnical University, Xi’an 710021, China · Harbin Engineering University, Harbin, Heilongjiang, China, and also with the School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China

  29. Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach

    Mar 9, 2026Yunhui Liu, Qizhuo Xie, Yinfeng Chen +4Anomaly DetectionGraph Neural Networks

    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China

  30. Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions

    Mar 9, 2026Aurelio Raffa Ugolini, Jessica Leoni, Valentina Breschi +4Anomaly DetectionInterpretable Anomaly Detection

    DEIB, Politecnico di Milano Via Giuseppe Ponzio 34, Milano, 20133 (Italy) · Dep. of Electrical Engineering, TU\e Groene Loper 19, Eindhoven, 5600 MB (The Netherlands) · Leonardo Labs, Leonardo S.p.A. Piazza Monte Grappa 4, Roma, 00195 (Italy)