Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts
Preserving authorship anonymity is paramount to protect activists, freedom of expression, and critical journalism. Although there are several mechanisms to provide anonymity on the Internet, one can still identify anonymous authors through their writing style. With the advances in neural network and...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Natural Language Processing Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949719124000554 |
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| author | Antônio Marcos Rodrigues Franco Ítalo Cunha Leonardo B. Oliveira |
| author_facet | Antônio Marcos Rodrigues Franco Ítalo Cunha Leonardo B. Oliveira |
| author_sort | Antônio Marcos Rodrigues Franco |
| collection | DOAJ |
| description | Preserving authorship anonymity is paramount to protect activists, freedom of expression, and critical journalism. Although there are several mechanisms to provide anonymity on the Internet, one can still identify anonymous authors through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when identifying the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. The first approach uses Generative Adversarial Networks to train an encoder–decoder to transform sentences from an input style into a target style. The second one trains an auto encoder with Gradient Reversal Layer to learn invariant representations. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our evaluation on real texts clarifies each technique’s trade-offs for Portuguese texts and provides guidance on practical deployment. |
| format | Article |
| id | doaj-art-3b563a19b0154d19821ce267ccf7a865 |
| institution | Kabale University |
| issn | 2949-7191 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Natural Language Processing Journal |
| spelling | doaj-art-3b563a19b0154d19821ce267ccf7a8652024-12-14T06:34:32ZengElsevierNatural Language Processing Journal2949-71912024-12-019100107Evaluation of deep neural network architectures for authorship obfuscation of Portuguese textsAntônio Marcos Rodrigues Franco0Ítalo Cunha1Leonardo B. Oliveira2Universidade Federal de Minas Gerais, Av. Presidente Antonio Carlos, 6627, Belo Horizonte, 31270010, Minas Gerais, BrazilCorresponding author.; Universidade Federal de Minas Gerais, Av. Presidente Antonio Carlos, 6627, Belo Horizonte, 31270010, Minas Gerais, BrazilUniversidade Federal de Minas Gerais, Av. Presidente Antonio Carlos, 6627, Belo Horizonte, 31270010, Minas Gerais, BrazilPreserving authorship anonymity is paramount to protect activists, freedom of expression, and critical journalism. Although there are several mechanisms to provide anonymity on the Internet, one can still identify anonymous authors through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when identifying the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. The first approach uses Generative Adversarial Networks to train an encoder–decoder to transform sentences from an input style into a target style. The second one trains an auto encoder with Gradient Reversal Layer to learn invariant representations. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our evaluation on real texts clarifies each technique’s trade-offs for Portuguese texts and provides guidance on practical deployment.http://www.sciencedirect.com/science/article/pii/S2949719124000554Authorship obfuscationPrivacyNatural language processingArtificial intelligence |
| spellingShingle | Antônio Marcos Rodrigues Franco Ítalo Cunha Leonardo B. Oliveira Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts Natural Language Processing Journal Authorship obfuscation Privacy Natural language processing Artificial intelligence |
| title | Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts |
| title_full | Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts |
| title_fullStr | Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts |
| title_full_unstemmed | Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts |
| title_short | Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts |
| title_sort | evaluation of deep neural network architectures for authorship obfuscation of portuguese texts |
| topic | Authorship obfuscation Privacy Natural language processing Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2949719124000554 |
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