Artifact Detection
1개 벤치마크 · 논문 86편 · 이 태스크의 논문 보기 →
Benchmarks
HistoArtifacts
Most implemented
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
Reference-based Restoration of Digitized Analog Videotapes
Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices
Detecting Human Artifacts from Text-to-Image Models
Papers
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 Detection