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.
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@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}
}
0000-0002-5284-1771