Authorship Attribution

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Overview

Authorship attribution studies how to identify a creator, decide whether two works share an author, or locate human and AI contributions within a work. Methods range from character and word n-grams and traditional stylometry to neural embeddings, contrastive learning, language-model likelihoods, and signals from handwriting, code, or narrative structure. These tools support literary and forensic analysis, privacy protection, and the detection of AI-generated or impersonated material. A central challenge is that performance can fall when genre, language, time, or model changes, while topic, editing, and mixed authorship can make stylistic evidence difficult to interpret.

Latest papers

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66 papers7 papers in the last four weeks, down 30% on the four weeks before. 0.0% of all new papers.

  1. Interpretable Predictability-Based AI Text Detection: A Replication Study

    Mar 16, 2026Adam Skurla, Dominik Macko, Jakub SimkoAI-Generated Text DetectionLanguage Model Fingerprinting

    Faculty of Information Technology, Brno University of Technology, Brno, Czechia · Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia

  2. Enhancing Authorship Attribution with Synthetic Paintings

    Mar 4, 2026Clarissa Loures, Caio Hosken, Luan Oliveira +2Authorship AttributionImage Classification

    Universidade Federal de Minas Gerais (UFMG), Brazil · Instituto Kunumi, Brazil

  3. Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification

    Feb 26, 2026David CondreyAuthorship AttributionBiometric Identification

    Writerslogic, Inc.

  4. Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent

    Feb 26, 2026Boyang Zhang, Yang ZhangPrivacy Leakage in Language ModelsAuthorship Attribution

    CISPA Helmholtz Center for Information Security

  5. Patch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings

    Feb 19, 2026Eric Chen, Patricia Alves-OliveiraAuthorship AttributionHuman-AI Co-Creation

    Robotics, University of Michigan, Ann Arbor, Michigan, United States.

  6. One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification

    Oct 15, 2025Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín +1StylometryAuthorship Attribution

    Department of Computer Systems Technical University of Madrid Madrid