The Role of Deepfake, Deception, and Disinformation in Conflict Zones Based on DL for NLP: A Critical AI-Era Perspective

The fast-paced growth of artificial intelligence (AI) has transformed contemporary information warfare, particularly in unstable, conf lict-ridden regions. Among the most significant threats is the “3D-Sec” triad Deepfake, Deception, and Disinformation which exploits synthetic media, psychological manipulation, and fabricated narratives to weaken digital trust, disrupt institutions, distort learning, and inf luence cognitive and socio-psychological behavior. This research rigorously examines the role of 3D-Sec in conf lict zones and introduces an AI-driven framework designed to identify and mitigate its repercussions on security, governance, education, cognition, and public perception. As NLP and AI-generated content becomes increasingly lifelike, the boundary between reality and fabrication blurs. Existing detection techniques often lack contextual understanding and fail to address the complex, multi-dimensional characteristics of 3D-Sec threats, including their cognitive and educational impacts. Uncontrolled 3D-Sec campaigns jeopardize privacy, obstruct peace efforts, misinform learners, and contribute to socio-psychological stress and geopolitical instability. Understanding these mechanisms is critical to protecting vulnerable populations, safeguarding education, and ensuring the integrity of digital communication during conf lict. We propose a Deep Learning–Natural Language Processing (DL-NLP) approach grounded in the ‘Multidimensional Knowledge Framework for Data Analysis (6-W)’. This framework incorporates six analytical dimensions, expressed as (Wt=f(Wy,Wr,Wn,Wo,Wh)) capturing the contextual, temporal, cognitive, and socio-psychological dependencies essential for detecting 3D-Sec activities. The proposed model enhances detection capabilities by integrating semantic, situational, and cognitive indicators, enabling accurate recognition of AI-induced deceit, misinformation, and educational content manipulation. A context-sensitive, interdisciplinary defense system such as this is vital for combating AI-enhanced information threats in war-torn regions, protecting cognition, education, and societal resilience against manipulation and misinformation.

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Publication Details

Journal
Dialnet (Universidad de la Rioja)
Published
2026-01-01
Primary Topic
Misinformation and Its Impacts
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article
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article

The Role of Deepfake, Deception, and Disinformation in Conflict Zones Based on DL for NLP: A Critical AI-Era Perspective

Pascal Muam Mah
Dialnet (Universidad de la Rioja)
Misinformation and Its Impacts
article

The Role of Deepfake, Deception, and Disinformation in Conflict Zones Based on DL for NLP: A Critical AI-Era Perspective

Pascal Muam Mah
article en

Abstract

The fast-paced growth of artificial intelligence (AI) has transformed contemporary information warfare, particularly in unstable, conf lict-ridden regions. Among the most significant threats is the “3D-Sec” triad Deepfake, Deception, and Disinformation which exploits synthetic media, psychological manipulation, and fabricated narratives to weaken digital trust, disrupt institutions, distort learning, and inf luence cognitive and socio-psychological behavior. This research rigorously examines the role of 3D-Sec in conf lict zones and introduces an AI-driven framework designed to identify and mitigate its repercussions on security, governance, education, cognition, and public perception. As NLP and AI-generated content becomes increasingly lifelike, the boundary between reality and fabrication blurs. Existing detection techniques often lack contextual understanding and fail to address the complex, multi-dimensional characteristics of 3D-Sec threats, including their cognitive and educational impacts. Uncontrolled 3D-Sec campaigns jeopardize privacy, obstruct peace efforts, misinform learners, and contribute to socio-psychological stress and geopolitical instability. Understanding these mechanisms is critical to protecting vulnerable populations, safeguarding education, and ensuring the integrity of digital communication during conf lict. We propose a Deep Learning–Natural Language Processing (DL-NLP) approach grounded in the ‘Multidimensional Knowledge Framework for Data Analysis (6-W)’. This framework incorporates six analytical dimensions, expressed as (Wt=f(Wy,Wr,Wn,Wo,Wh)) capturing the contextual, temporal, cognitive, and socio-psychological dependencies essential for detecting 3D-Sec activities. The proposed model enhances detection capabilities by integrating semantic, situational, and cognitive indicators, enabling accurate recognition of AI-induced deceit, misinformation, and educational content manipulation. A context-sensitive, interdisciplinary defense system such as this is vital for combating AI-enhanced information threats in war-torn regions, protecting cognition, education, and societal resilience against manipulation and misinformation.

Dialnet (Universidad de la Rioja)
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Misinformation and Its Impacts
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