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

ABSTRACTThe fast-paced growth of artificial intelligence (AI) has transformed contemporary information warfare, particularly in unstable, conflict-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 influence cognitive and socio-psychological behavior. This research rigorously examines the role of 3D-Sec in conflict 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 conflict. 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.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-04
DOI
https://doi.org/10.5281/zenodo.18115680
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Pascal Muam Mah
Zenodo (CERN European Organization for Nuclear Research)
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

ABSTRACTThe fast-paced growth of artificial intelligence (AI) has transformed contemporary information warfare, particularly in unstable, conflict-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 influence cognitive and socio-psychological behavior. This research rigorously examines the role of 3D-Sec in conflict 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 conflict. 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.

Zenodo (CERN European Organization for Nuclear Research)
AGH University of Krakow (PL)
Quality Education
Openalex Percentile: Top 99%
Misinformation and Its Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.