Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence

Title

Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence

Creator

Giri M.; Eswaran S.; Honnavalli P.; D D.

Description

Fake news is a piece of misleading or forged information that affects society, business, governments, etc., hence is an imperative issue. The solution presented here to detect fake news involves purely using rigorous machine learning approaches in implementing a hybrid of simple yet accurate fake text detection models and fake image detection models to detect fake news. The solution considers the text and images of any news article, extracted using web scraping, where the text segment of a news article is analyzed using an ensemble model of the Nae Bayes, Random Forest, and Decision Tree classifier, which showed improved results than the individual models. The image segment of a news article is analyzed using only a Convolution Neural Network, which showed optimal accuracy similar to the text model. To better train the text models, data preprocessing and aggregation methods were used to combine various fake-real news datasets to have ample amounts of data. Similarly, the CASIA dataset was used to train the image model, over which Error Level Analysis was performed to detect fake images. model results are represented as confusion matrices and are measured using various performance metrics. Also, to explain predictions from the hybrid model, Explainable Artificial Intelligence is used. 2024 Taylor & Francis Group, LLC.

Source

Journal of Applied Security Research, Vol-19, No. 4, pp. 628-648.

Date

2024-01-01

Publisher

Routledge

Subject

Convolution Neural Network; Decision Tree; ensemble model; error level analysis; explainable AI; Nae Bayes classifier; Random Forest classifier

Coverage

Giri M., Research Center for Information Security, Forensics and Cyber Resilience, PES University, Karnataka, Bangalore, India; Eswaran S., Department of Electrical and Computer Engineering, Curtin University, Sarawak, Miri, Malaysia; Honnavalli P., Research Center for Information Security, Forensics and Cyber Resilience, PES University, Karnataka, Bangalore, India; D D., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 19361610

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1080/19361610.2024.2356431

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