| Téléchargement | - Voir le manuscrit accepté : Effect of text augmentation and adversarial training on fake news detection (PDF, 1,5 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1109/TCSS.2023.3344597 |
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| Auteur | Rechercher : Ahmed, HadeerIdentifiant ORCID : https://orcid.org/0000-0001-9568-1563; Rechercher : Traore, IssaIdentifiant ORCID : https://orcid.org/0000-0003-2987-8047; Rechercher : Saad, SherifIdentifiant ORCID : https://orcid.org/0000-0002-5506-5261; Rechercher : Mamun, Mohammad1Identifiant ORCID : https://orcid.org/0000-0002-4045-8687 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Sujet | meaning representation (AMR); adversarial training; clonal selection algorithm; cyberattack; fake news; graph representation; text generation; data models; training; machine learning; training data; data augmentation; social networking (online) |
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| Résumé | The action of spreading false information through fake news articles presents a significant danger to society because it has the ability to shape public opinion with inaccurate facts. This can lead to negative effects, such as reduced trust in institutions and the promotion of conflict, division, and even violence. In this article, a text augmentation technique is introduced as a means of generating new data from preexisting fake news datasets. This approach has the potential to enhance classifier performance by a range of 3%–11%. It can also be utilized to launch a successful attack on trained classifiers, with up to a 90% success rate. However, the success rate of these attacks decreased to less than 28% when the model was retrained with the generated adversarial examples. These results demonstrate the effectiveness of text augmentation as a viable method for detecting fake news and increasing classifier accuracy and performance, as well as its ability to be utilized to perform adversarial machine learning (ML) and improve the resilience of ML algorithms. |
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| Date de publication | 2024-01-12 |
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| Maison d’édition | Institute of Electrical and Electronics Engineers |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 34458b13-f5d9-4c67-8549-781b36951a8d |
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| Enregistrement créé | 2024-04-03 |
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| Enregistrement modifié | 2024-10-16 |
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