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In recent years, the increasing use of Artificial Intelligence based textgeneration tools has posed new challenges in document provenance,authentication, and authorship detection. However, advancements in stylometryhave provided opportunities for automatic authorship and author changedetection in multi-authored documents using style analysis techniques. Styleanalysis can serve as a primary step toward document provenance andauthentication through authorship detection. This paper investigates three keytasks of style analysis: (i) classification of single and multi-authoreddocuments, (ii) single change detection, which involves identifying the pointwhere the author switches, and (iii) multiple author-switching detection inmulti-authored documents. We formulate all three tasks as classificationproblems and propose a merit-based fusion framework that integrates severalstate-of-the-art natural language processing (NLP) algorithms and weightoptimization techniques. We also explore the potential of special characters,which are typically removed during pre-processing in NLP applications, on theperformance of the proposed methods for these tasks by conducting extensiveexperiments on both cleaned and raw datasets. Experimental results demonstratesignificant improvements over existing solutions for all three tasks on abenchmark dataset.

Stylometry Analysis of Multi-authored Documents for Authorship and Author Style Change Detection