Search Results for author:
Found 9 papers, 3 papers with code
Authorship Verification based on the Likelihood Ratio of Grammar Models
Existing state-of-the-art AV methods use computational solutions that are not supported by a plausible scientific explanation for their functioning and that are often difficult for analysts to interpret.
Face Pasting Attack
For an attack to be considered successful the target class has to have the highest confidence among all classes and the stealthiness has to be at least 0. 5.
TAVeer: An Interpretable Topic-Agnostic Authorship Verification Method
A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author.
A Step Towards Interpretable Authorship Verification
A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author.
POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis
In recent years, a variety of AV methods have been proposed that focus on this problem and can be divided into two categories: The first category refers to such methods that are based on explicitly defined features, where one has full control over which features are considered and what they actually represent.
Assessing the Applicability of Authorship Verification Methods
Our results indicate that part of the methods are able to cope with very challenging verification cases such as 250 characters long informal chat conversations (72. 7% accuracy) or cases in which two scientific documents were written at different times with an average difference of 15. 6 years (> 75% accuracy).
Unary and Binary Classification Approaches and their Implications for Authorship Verification
Even though AV represents a unary classification problem, a number of existing approaches consider it as a binary classification task.
Authorship verification in the absence of explicit features and thresholds
Moreover, many existing AV methods are based on explicit thresholds (needed to accept or reject a stated authorship), which are determined on training corpora.
Authorship Verification based on Compression-Models
Instead, the only three key components of our method are a compressing algorithm, a dissimilarity measure and a threshold, needed to accept or reject the authorship of the questioned document.
