Datasets and Models for Authorship Attribution on Italian Personal Writings

Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.Comment: Accepted for publication in: 7th Italian Conference on Computational Linguistics (CLIC-IT 2020

Personal information prediction from written texts

La détection de la paternité textuelle est un domaine de recherche qui existe depuis les années 1960. Il consiste à prédire l’auteur d’un texte en se basant sur d’autres textes dont les auteurs sont connus. Pour faire cela, plusieurs traits sur le style d’écriture et le contenu sont extraits. Pour ce mémoire, deux sous-problèmes de détection de la paternité textuelle ont été traités : la prédiction du genre et de l’âge de l’auteur. Des données collectées de blogs en ligne ont été utilisées pour faire cela. Dans ce travail, plusieurs traits (features) textuels ont été comparé en utilisant des méthodes d’apprentissage automatique. De même, des méthodes d’apprentissage profond ont été appliqués. Pour la tâche de classification du genre, les meilleurs résultats ont été obtenus en appliquant un système de vote majoritaire sur la prédiction d’autres modèles. Pour la classification d’âge, les meilleurs résultats ont été obtenu en utilisant un classificateur entrainé sur TF-IDF.Authorship Attribution (AA) is a field of research that exists since the 60s. It consists of identifying the author of a certain text based on texts with known authors. This is done by extracting features about the writing style and the content of the text. In this master thesis, two sub problems of AA were treated: gender and age classification using a corpus collected from online blogs. In this work, several features were compared using several feature-based algorithms. As well as deep learning methods. For the gender classification task, the best results are the ones obtained by a majority vote system over the outputs of several classifiers. For the age classification task, the best result was obtained using classifier trained over TFIDF

Datasets and Models for Authorship Attribution on Italian Personal Writings

Existing research on Authorship Attribution (AA) focuses on texts for which a lot of data is available (e.g novels), mainly in English. We approach AA via Authorship Verification on short Italian texts in two novel datasets, and analyze the interaction between genre, topic, gender and length. Results show that AV is feasible even with little data, but more evidence helps. Gender and topic can be indicative clues, and if not controlled for, they might overtake more specific aspects of personal style.Comment: Accepted for publication in: 7th Italian Conference on Computational Linguistics (CLIC-IT 2020

Continuous User Authentication Using Multi-Modal Biometrics

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data

Linguistic identifiers of L1 Persian speakers writing in English:NLID for authorship analysis

This research focuses on Native Language Identification (NLID), and in particular, on the linguistic identifiers of L1 Persian speakers writing in English. This project comprises three sub-studies; the first study devises a coding system to account for interlingual features present in a corpus of L1 Persian speakers blogging in English, and a corpus of L1 English blogs. Study One then demonstrates that it is possible to use interlingual identifiers to distinguish authorship by L1 Persian speakers. Study Two examines the coding system in relation to the L1 Persian corpus and a corpus of L1 Azeri and L1 Pashto speakers. The findings of this section indicate that the NLID method and features designed are able to discriminate between L1 influences from different languages. Study Three focuses on elicited data, in which participants were tasked with disguising their language to appear as L1 Persian speakers writing in English. This study indicated that there was a significant difference between the features in the L1 Persian corpus, and the corpus of disguise texts. The findings of this research indicate that NLID and the coding system devised have a very strong potential to aid forensic authorship analysis in investigative situations. Unlike existing research, this project focuses predominantly on blogs, as opposed to student data, making the findings more appropriate to forensic casework data