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World Journal of Advanced Research and Reviews
Article . 2025 . Peer-reviewed
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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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The misinformation epidemic: combating AI-generated fake content and deepfakes

Authors: Aditya, Shivam;

The misinformation epidemic: combating AI-generated fake content and deepfakes

Abstract

This article examines the escalating threat of AI-generated deepfakes and synthetic media to global information ecosystems, democratic processes, and financial stability. The article traces the technical evolution of deepfake technologies from early experimental models to widely accessible creation tools, analyzing their amplification through algorithmic systems and exploitation of human cognitive vulnerabilities. Through a comprehensive threat analysis framework, the article identifies critical technological and social vulnerabilities while assessing specific risks to democratic institutions, media ecosystems, and public trust. The article presents a multilayered response strategy integrating blockchain authentication systems, neural network detection algorithms, international regulatory frameworks, and targeted media literacy initiatives. The article evaluates emerging countermeasure technologies, including cryptographic content verification, metadata analysis approaches, and real-time detection systems, while proposing governance structures capable of cross-border enforcement. Looking toward future developments, the article explores self-regulating AI systems with built-in verification mechanisms and cross-disciplinary intervention models that bridge technical, social, and regulatory domains. This integrated approach offers a sustainable framework for preserving information integrity against increasingly sophisticated synthetic media challenges.

Keywords

Synthetic Media Authentication, Algorithmic Amplification, Media Literacy Interventions., Cross-Border Regulatory Frameworks, Deepfake Detection

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
gold