🤖 AI Summary
This work proposes CHiPS, a lightweight, preprocessing-free, and leakage-resistant authorship attribution method tailored for Romanian text. CHiPS uniquely transforms character positional signals into frequency-domain features (FFT12-LR) and combines them with character histogram-based modeling (CH-SVM) to capture stylistic patterns—eliminating the need for tokenization, syntactic parsing, or pretrained language models. The approach introduces a novel decision-level fusion mechanism designed to be leakage-safe, achieving high transparency and controllability with minimal feature engineering. Evaluated on the ROST and ROSTories-cleaned datasets, CHiPS attains accuracy and macro F1 scores of 0.9310/0.9341 and 0.8919/0.8708, respectively, demonstrating strong performance under stringent data integrity constraints.
📝 Abstract
We propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. All reported experiments are closed-set: the true author is one of the candidate authors in the training data. CHiPS studies two complementary fingerprints of writing style: CH-SVM, a character-histogram classifier based on one-character marginal distributions, and FFT12-LR, a positional-signal classifier that represents selected characters and punctuation classes as impulse trains (binary indicator sequences over character positions) and extracts Fourier/Welch spectral descriptors. We also report CHiPS-F, a leakage-safe decision-level fusion variant, and an optional top-5 listwise reranker trained only on out-of-fold predictions. The method requires no tokenization, syntactic analysis, pretrained language model, or transformer fine-tuning, and it avoids character $n$-gram features with $n \geq 2$ in the histogram component. On a locked grouped ROST split comprising 400 files from 392 source-text groups, written by 10 authors, with source-text-level evaluation and grouped five-fold model selection, CHiPS-F reaches 0.9310 accuracy and 0.9341 macro-F1. A matched but unrestricted character 2--5-gram TF--IDF SVM comparator reaches 1.0000 accuracy and macro-F1 on the same held-out groups, so the contribution is not a claim of best possible classification accuracy. Instead, the experiments ask how far restricted, transparent character evidence can go under strict leakage control. On ROSTories-cleaned, a secondary ROST-overlapping corpus comprising 1,248 files from 1,240 source-text groups, written by 19 authors, the same protocol gives 0.8919 accuracy and 0.8708 macro-F1 for CHiPS-R.