Papers Text Detection
“Text Detection” 태그가 달린 논문 728편 · 필터 해제
Beyond Global Scalars: Synergizing Token-Level Statistics and Deep Semantics for Adversarial AIGC Text Detection
The rapid evolution of large language models necessitates robust machine-generated text detection. Existing paradigms typically follow two isolated tracks. Training-free methods rely on global statistical scalars such as…
Text DetectionD2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection
AI-generated text detection is commonly framed as a binary document-level judgment about whether a text is human-written or machine-generated. This framing breaks down for mixed-origin writing, where content origin and e…
Text DetectionRelational Over-Regularization: Graph-Based AI-Generated Text Detection via Sentence Transition Deviation
Detecting AI-generated text (AIGT) remains challenging because existing approaches rely on token-level statistical signals or independent stylometric features, causing them to overfit to specific generators and fail unde…
Text DetectionUnveiling Spectral Mechanisms in Training-Free LLM Text Detection
The rapid advancement of Large Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training-free detection offers a scalable solution, yet common confidence-ba…
Text DetectionDeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text
The rapid spread of large language models (LLMs) across the web raises concerns about misinformation, academic integrity, automated content manipulation, and risks to vulnerable online communities. Existing transformer-b…
Text DetectionASK-NN: An Asymmetric Nearest-Neighbor Test that detects Distribution Drifts in Natural Language
Hallucinations and artificial text in LLM-generated outputs often appear as distributional deviations between prompt and response hidden-state distributions. Since prompts or retrieved contexts typically serve as referen…
Text DetectionLatent Trajectory Discrimination for AI-Generated Text Detection
Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or globally compressed embeddings. However, this perspective overlooks th…
Contrastive LearningText DetectionShow Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Given the current trend to employ large language models (LLMs) in almost any imaginable context, LLM-generated text detection and authorship attribution have become a pressing issue. Prior work has primarily focused on s…
Graph Neural NetworkArgument MiningText DetectionMonkeyOCRv2: A Visual-Text Foundation Model for Document AI
Mainstream visual encoders are pretrained on natural images and cannot be effectively applied to document images without document-oriented adaptation, as dense text and fine-grained character strokes demand character-lev…
Text GenerationText DetectionDocument AIRethinking AI-Generated Text Detection: A Strong Baseline and the Distribution-Shift Problem That Remains
Recent AI-generated text detection work often introduces a new benchmark together with a specialized detector tailored to it. We revisit this practice from a baseline-first perspective. Across several benchmarks, we show…
Domain AdaptationText DetectionTriospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect Detection Framework by using additional perspectives of content (core id…
Text DetectionSTEB: Style Text Embedding Benchmark
While semantic embeddings are rigorously evaluated on the Massive Text Embedding Benchmark, the evaluation of style embeddings remains fragmented, with each work relying on their own set of tasks and datasets. To bridge …
Text DetectionHitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
Deployed approaches for AI text detection often rely on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable to three types of distribution shifts that occur c…
Test-time AdaptationText DetectionWaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform
As Large Language Models asymptotically approach human-level fluency in natural language generation, solely relying on surface-level semantic artifacts for detecting LLM-generated texts has become increasingly precarious…
Text DetectionSenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents
Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependen…
Structured PredictionText DetectionSignature filtering: a lightweight enhancement for statistical watermark detection in large language models
Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited. We propose si…
Text GenerationText DetectionText region detection in historical astronomical diagrams
Text detection is a crucial task in the analysis of historical documents. While datasets and benchmarks exist for text detection in manuscripts and maps, the study of text in mathematical diagrams has received little att…
Text DetectionNon-Parametric Machine Text Detection via Multi-View Gaussian Processes
Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors. A document, however, carries multiple complementary signals (e.g., stylistic features, likel…
Gaussian ProcessesText DetectionStyle TransferUnsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion
The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors. Recent work has sho…
Representation LearningText DetectionSV-Detect: AI-generated Text Detection with Steering Vectors
Detecting machine-generated text is especially difficult under distribution shift, such as transfer across domains, source models, and editing attacks. We propose a fake-text detector based on steering vectors extracted …
Text Detection