2023
Conference article  Open Access

Enhancing adversarial authorship verification with data augmentation

Corbara S., Moreo A.

Authorship verification  Data augmentation  Text classification 

It has been shown that many Authorship Identification systems are vulnerable to adversarial attacks, where an author actively tries to fool the classifier. We propose to tackle the adversarial Authorship Verification task by augmenting the training set with synthetic textual examples. In this ongoing study, we present preliminary results using two learning algorithms (SVM and Neural Network), and two generation strategies (based on language modeling and GAN training) for two generator models, on three datasets. We empirically show that data augmentation may help improve the performance of the classifier in an adversarial setup.

Source: IIR 2023 - 13th Italian Information Retrieval Workshop, pp. 73–78, Pisa, Italy, 8-9/6/23.



Back to previous page
BibTeX entry
@inproceedings{oai:it.cnr:prodotti:486039,
	title = {Enhancing adversarial authorship verification with data augmentation},
	author = {Corbara S. and Moreo A.},
	booktitle = {IIR 2023 - 13th Italian Information Retrieval Workshop, pp. 73–78, Pisa, Italy, 8-9/6/23.},
	year = {2023}
}
CNR ExploRA

Bibliographic record

ISTI Repository

Published version Open Access

Also available from

ceur-ws.orgOpen Access