The Role of Deepfake, Deception, and Disinformation in Conflict Zones Based on DL for NLP: A Critical AI-Era Perspective
DOI:
https://doi.org/10.5281/zenodo.18115680Keywords:
3D-Sec, Deepfake Detection, Disinformation Warfare, Artificial Intelligence in Conflict, Context-Aware NLP, Multidimensional Knowledge Framework, Deep LearningAbstract
The 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.
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