Papers Artifact Detection
“Artifact Detection” 태그가 달린 논문 86편 · 필터 해제
DOSE-I: A Multimodal Biosignal Dataset of Procedural Sedation for Endoscopy -- Technical Report
In this document, we describe characteristics and technical details of the multimodal biosignal dataset DOSE-I of procedural sedation for endoscopy published on zenodo. The DOSE-I dataset includes 78.5 hours of recording…
Artifact DetectionSalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images
Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze such artifacts remains poorly understood. A correct image-level decision c…
Artifact DetectionNeed We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations. Under optimization on limited samples, models overfit to local domain shortcuts. Fine-tuni…
Binary ClassificationArtifact DetectionArtifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural distortions, and semantic incoherence. While…
Video ClassificationArtifact DetectionReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation
The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image forgery detection systems. Existing methods, whether non-LLM-based or LLM-b…
Contrastive LearningArtifact DetectionnASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio which makes extraction of meaningful neural information challenging. Artifact Subspace Reconstruction (ASR)…
Artifact DetectionIPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts
Current image quality assessment methods are heavily biased towards global distortions (e.g., noise, blur), neglecting local perceptual artifacts such as ghosting, lens flare, and moire effects. Although significant prog…
Image Quality AssessmentArtifact DetectionAnomaly DetectionSemantic Manipulation Localization
Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and generative models, many manipulations no longer exhibit obvious low-level ar…
Image Manipulation LocalizationArtifact DetectionImage EditingStructural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis
SVG generation is typically evaluated by comparing rendered outputs to reference images, which captures visual similarity but not the structural properties that make SVG editable, decomposable, and reusable. Inspired by …
Artifact DetectionSlice-wise quality assessment of high b-value breast DWI via deep learning-based artifact detection
Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast magnetic resonance imaging (MRI), however especially high b-value diffusion-weighted acquisitions can be prone to intensity art…
Binary ClassificationArtifact DetectionArtifactLens: Hundreds of Labels Are Enough for Artifact Detection with VLMs
Modern image generators produce strikingly realistic images, where only artifacts like distorted hands or warped objects reveal their synthetic origin. Detecting these artifacts is essential: without detection, we cannot…
Artifact DetectionArtifact Removal and Image Restoration in AFM:A Structured Mask-Guided Directional Inpainting Approach
Atomic Force Microscopy (AFM) enables high-resolution surface imaging at the nanoscale, yet the output is often degraded by artifacts introduced by environmental noise, scanning imperfections, and tip-sample interactions…
Semantic SegmentationArtifact DetectionImage RestorationDNA: Uncovering Universal Latent Forgery Knowledge
As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones, we propose that forgery detection capab…
Artifact DetectionArtifact-Aware Evaluation for High-Quality Video Generation
With the rapid advancement of video generation techniques, evaluating and auditing generated videos has become increasingly crucial. Existing approaches typically offer coarse video quality scores, lacking detailed local…
Artifact DetectionVideo GenerationDiffusionQC: Artifact Detection in Histopathology via Diffusion Model
Digital pathology plays a vital role across modern medicine, offering critical insights for disease diagnosis, prognosis, and treatment. However, histopathology images often contain artifacts introduced during slide prep…
Contrastive LearningArtifact DetectionForensicFormer: Hierarchical Multi-Scale Reasoning for Cross-Domain Image Forgery Detection
The proliferation of AI-generated imagery and sophisticated editing tools has rendered traditional forensic methods ineffective for cross-domain forgery detection. We present ForensicFormer, a hierarchical multi-scale fr…
Artifact DetectionAutomated Motion Artifact Check for MRI (AutoMAC-MRI): An Interpretable Framework for Motion Artifact Detection and Severity Assessment
Motion artifacts degrade MRI image quality and increase patient recalls. Existing automated quality assessment methods are largely limited to binary decisions and provide little interpretability. We introduce AutoMAC-MRI…
Contrastive LearningArtifact DetectionAlignGemini: Generalizable AI-Generated Image Detection Through Task-Model Alignment
Vision Language Models (VLMs) are increasingly used for detecting AI-generated images (AIGI). However, converting VLMs into reliable detectors is resource-intensive, and the resulting models often suffer from hallucinati…
Artifact DetectionTowards Unified Video Quality Assessment
Recent works in video quality assessment (VQA) typically employ monolithic models that typically predict a single quality score for each test video. These approaches cannot provide diagnostic, interpretable feedback, off…
Video Quality AssessmentArtifact DetectionKnowledge-based anomaly detection for identifying network-induced shape artifacts
Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artifacts, distortions, and unrealistic featu…
Artifact DetectionAnomaly Detection