Background-Aware Defect Generation for Robust Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2411.16767v2
• Date: Fri, 28 Feb 2025 09:29:21 GMT
• Title: Background-Aware Defect Generation for Robust Industrial Anomaly Detection
• Authors: Youngjae Cho, Gwangyeol Kim, Sirojbek Safarov, Seongdeok Bang, Jaewoo Park,
• Abstract summary: Generative models can mitigate this issue by synthesizing realistic defect samples.<n>Existing approaches often fail to model the crucial interplay between defects and their background.<n>We propose a novel background-aware defect generation framework, where the background influences defect denoising without affecting the background itself.
• Score: 5.247863056798329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting anomalies in industrial settings is challenging due to the scarcity of labeled anomalous data. Generative models can mitigate this issue by synthesizing realistic defect samples, but existing approaches often fail to model the crucial interplay between defects and their background. This oversight leads to unrealistic anomalies, especially in scenarios where contextual consistency is essential (i.e., logical anomaly). To address this, we propose a novel background-aware defect generation framework, where the background influences defect denoising without affecting the background itself by ensuring realistic synthesis while preserving structural integrity. Our method leverages a disentanglement loss to separate the background' s denoising process from the defect, enabling controlled defect synthesis through DDIM Inversion. We theoretically demonstrate that our approach maintains background fidelity while generating contextually accurate defects. Extensive experiments on MVTec AD and MVTec Loco benchmarks validate our mehtod's superiority over existing techniques in both defect generation quality and anomaly detection performance.

Related papers

• Lost in the Noise: How Reasoning Models Fail with Contextual Distractors [57.31788955167306]
  Recent advances in reasoning models and agentic AI systems have led to an increased reliance on diverse external information.<n>We introduce NoisyBench, a comprehensive benchmark that systematically evaluates model robustness across 11 datasets in RAG, reasoning, alignment, and tool-use tasks.<n>Our evaluation reveals a catastrophic performance drop of up to 80% in state-of-the-art models when faced with contextual distractors.
  arXiv  Detail & Related papers  (2026-01-12T05:43:51Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learning [15.311912138752431]
  Integrated circuit manufacturing is highly complex. Defects can arise at any stage, causing yield loss and degrading product reliability.<n>We propose IC DefectNCD, a support set free framework that leverages Image Intrinsic Priors in IC SEM images for defect detection and novel class discovery.
  arXiv  Detail & Related papers  (2025-11-05T02:10:08Z)
• Quality-Aware Language-Conditioned Local Auto-Regressive Anomaly Synthesis and Detection [30.77558600436759]
  ARAS is a language-conditioned, auto-regressive anomaly synthesis approach.<n>It injects local, text-specified defects into normal images via token-anchored latent editing.<n>It significantly enhances defect realism, preserves fine-grained material textures, and provides continuous semantic control over synthesized anomalies.
  arXiv  Detail & Related papers  (2025-08-05T15:07:32Z)
• Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection [53.137651284042434]
  Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples limits the effectiveness of existing methods.<n>We propose Generate grained Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework.<n>GAA generates realistic, diverse, and semantically aligned anomalies using only a small number of samples.
  arXiv  Detail & Related papers  (2025-07-13T12:56:59Z)
• DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection [32.80355689164011]
  DefectFill is a novel method for realistic defect generation that requires only a few reference defect images. It enables precise capture of detailed, localized defect features and their seamless integration into defect-free objects.
  arXiv  Detail & Related papers  (2025-03-18T07:42:11Z)
• PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness [10.364634539199422]
  We introduce PA-CLIP, a zero-shot anomaly detection method that reduces background noise and enhances defect detection through a pseudo-anomaly-based framework. The proposed method integrates a multiscale feature aggregation strategy for capturing detailed global and local information. It outperforms existing zero-shot methods, providing a robust solution for industrial defect detection.
  arXiv  Detail & Related papers  (2025-03-03T08:29:27Z)
• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• Few-shot Defect Image Generation based on Consistency Modeling [1.8029094254659288]
  DefectDiffu is a novel text-guided diffusion method to model defect consistency across multiple products. We show that DefectDiffu surpasses state-of-the-art methods in terms of generation quality and diversity.
  arXiv  Detail & Related papers  (2024-08-01T08:29:42Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [89.49600182243306]
  We reformulate the reconstruction process using a diffusion model into a noise-to-norm paradigm. We propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection [4.873362301533825]
  We propose a Lossy Causal Temporal Convolutional Neural Network Autoencoder for anomaly detection. Our framework uses a rate-distortion loss and an entropy bottleneck to learn a compressed latent representation for the task.
  arXiv  Detail & Related papers  (2022-12-05T14:29:16Z)
• Unsupervised Visual Defect Detection with Score-Based Generative Model [17.610722842950555]
  We focus on the unsupervised visual defect detection and localization tasks. We propose a novel framework based on the recent score-based generative models. We evaluate our method on several datasets to demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-11-29T11:06:29Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection [34.699695525216185]
  Defect-GAN is an automated defect synthesis network that generates realistic and diverse defect samples. It learns through defacement and restoration processes, where the defacement generates defects on normal surface images. It can also mimic variations of defects and offer flexible control over the locations and categories of the generated defects.
  arXiv  Detail & Related papers  (2021-03-28T15:53:34Z)
• Semi-supervised Anomaly Detection using AutoEncoders [0.0]
  Anomaly detection refers to the task of finding unusual instances that stand out from the normal data. In this paper, we present a convolutional auto-encoder architecture for anomaly detection that is trained only on the defect-free (normal) instances. The approach was tested on two data-sets and achieved an impressive average F1 score of 0.885.
  arXiv  Detail & Related papers  (2020-01-06T23:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.