Reconstruction-Based Anomaly Detection

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22 papers in the last four weeks, up 267% on the four weeks before. 0.1% of all new papers.

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Detecting AI-Generated Forgeries via Iterative Manifold Deviation Amplification
Feb 21, 2026Jiangling Zhang, Shuxuan Gao, Bofan Liu +4Image Forgery Localization Digital Image Forensics
1Wuhan University of Technology
A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems
Feb 17, 2026Nasim Abdirahman Ismail, Enis KaraarslanFinancial Fraud Detection Generative Adversarial Networks
Mugla Sitki Kocman University · Department of Computer Engineering, Mugla Sitki Kocman University
Fault Detection in Electrical Distribution System using Autoencoders
Feb 16, 2026Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende +2Fault Detection Power Systems
ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, 500084, India. · School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, 751013, India. · Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad, 500078, India.
COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing
Nov 10, 2025Wenlong Shang, Shihao Tian, Xutong Wan +1Reconstruction-Based Anomaly Detection Time-Series Anomaly Detection
School of Information Science and Technology, Beijing University of Technology, China · Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, China
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
Aug 27, 2025Luhu Li, Bin Liu, Bowen Lin +3Prototype Learning Medical Image Anomaly Detection
School of Mathematics, Shandong University, Jinan 250100, China · Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University, Jinan 250033, China · Department of Neurosurgery, The Second Qilu Hospital of Shandong University,2026 Jinan 250033, China
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection
Dec 4, 2024Xiaofeng Tan, Xin Geng, Liang Wang +1Video Anomaly Detection Reconstruction-Based Anomaly Detection
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA) +1
ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data
Aug 28, 2024Weizhou Wang, Eric Liu, Xiangyu Guo +3Software Vulnerability Detection LLMs for Cybersecurity
University of Toronto, Toronto, Canada

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Detecting AI-Generated Forgeries via Iterative Manifold Deviation Amplification

Jiangling Zhang, Shuxuan Gao, Bofan Liu, Siqiang Feng+3

1Wuhan University of Technology

Image Forgery Localization Digital Image Forensics Reconstruction-Based Anomaly Detection

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The proliferation of highly realistic AI-generated images poses critical challenges for digital forensics, demanding precise pixel-level localization of manipulated regions. Existing methods predominantly learn discriminative patterns of specific forgeries and often struggle with novel manipulations as editing techniques continue to evolve. We propose the Iterative Forgery Amplifier Network (IFA-Net), which shifts from learning "what is fake" to modeling "what is real". Grounded in the principle that all manipulations deviate from the natural image manifold, IFA-Net leverages a frozen Masked Autoencoder (MAE) pretrained on real images as a universal realness prior. Our framework operates through a two-stage closed-loop process: an initial Dual-Stream Segmentation Network (DSSN) fuses the original image with MAE reconstruction residuals for coarse localization, followed by a Task-Adaptive Prior Injection (TAPI) module that converts this coarse prediction into guiding prompts to steer the MAE decoder and amplify reconstruction failures in suspicious regions for precise refinement. Extensive experiments on four diffusion-based inpainting benchmarks show that IFA-Net achieves an average improvement of 6.5% in IoU and 8.1% in F1-score over the second-best method, while demonstrating strong generalization to traditional manipulation types.