15 citations · 45 across the 7 of their papers we have counts for
7 papers
Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification
Andrea Nini, Oren Halvani, Lukas Graner +3
Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were written by the same person. Num…
Face Pasting Attack
Niklas Bunzel, Lukas Graner
Cujo AI and Adversa AI hosted the MLSec face recognition challenge. The goal was to attack a black box face recognition model with targeted attacks. The model returned the confiden…
A Step Towards Interpretable Authorship Verification
Oren Halvani, Lukas Graner, Roey Regev
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. Authorship v…
POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis
Oren Halvani, Lukas Graner
Authorship verification (AV) is a fundamental research task in digital text forensics, which addresses the problem of whether two texts were written by the same person. In recent y…
Assessing the Applicability of Authorship Verification Methods
Oren Halvani, Christian Winter, Lukas Graner
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 sam…
Unary and Binary Classification Approaches and their Implications for Authorship Verification
Oren Halvani, Christian Winter, Lukas Graner
Retrieving indexed documents, not by their topical content but their writing style opens the door for a number of applications in information retrieval (IR). One application is to…
Authorship Verification based on Compression-Models
Oren Halvani, Christian Winter, Lukas Graner
Compression models represent an interesting approach for different classification tasks and have been used widely across many research fields. We adapt compression models to the fi…