3260 papers • 126 benchmarks • 313 datasets
Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. Definition taken from the paper Assessing the Applicability of Authorship Verification Methods, available at: https://arxiv.org/abs/1906.10551
(Image credit: Papersgraph)
These leaderboards are used to track progress in authorship-verification
No benchmarks available.
Use these libraries to find authorship-verification models and implementations
No subtasks available.
This work proposes a new neural network topology for similarity learning that significantly improves the performance on the author verification task with such challenging data sets.
It is revealed that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream, and applications of the results include partisanship detection and pre-screening for semi-automatic fake news detection.
A novel method is presented that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures to mask topic-specific information that is not related to the personal style of authors.
This work proposes an intrinsic AV method, which yields competitive results compared to a number of current state-of-the-art approaches, based on support vector machines or neural networks, and can handle complicated AV cases where both, the questioned and the reference document, are not related to each other in terms of topic or genre.
A novel parameter-free AV approach is proposed, which derives its thresholds for each verification case individually and enables AV in the absence of explicit features and training corpora.
This paper presents a generalized unmasking approach which allows for authorship verification of texts as short as four printed pages with very high precision at an adjustable recall tradeoff, making unmasking applicable to authorship cases of more practical proportions.
A new obfuscation approach models writing style difference as the Jensen-Shannon distance between the character n-gram distributions of texts, and manipulates an author’s subconsciously encoded writing style in a sophisticated manner using heuristic search.
This work proposes a substantial extension of a recently published hierarchical Siamese neural network approach, with which it is feasible to learn neural features and to visualize the decision-making process and shows that the proposed method is indeed able to latch on to some traditional linguistic categories.
Adding a benchmark result helps the community track progress.