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{\bf A B S T R A C T}
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The proliferation of disinformation during armed conflicts poses unprecedented challenges to global information ecosystems and threatens international stability. Modern hybrid warfare increasingly relies on sophisticated multimedia manipulation---including deepfakes, fabricated audio-video content, coordinated social media campaigns, and semantically deceptive imagery---to shape narratives, manipulate public opinion, and escalate geopolitical tensions. Traditional fact-checking approaches, reliant on manual verification, cannot scale to handle the volume, velocity, and multimodal complexity of weaponized disinformation in conflict zones.

This thesis presents a comprehensive hierarchical multimodal framework for detecting disinformation in armed conflict contexts, integrating \textbf{ten independent detection modules} through a sophisticated late-fusion architecture with multi-level decision-making. The system unifies:

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    \item \textbf{Image Authenticity Analysis}: Optimal 3-Level Hierarchical Ensemble combining CNN forensics (ResNet/Xception), CLIP-based image-caption consistency, and LLM visual reasoning with equal-weight score fusion achieving 92\% accuracy
    \item \textbf{Social Media Impact Analysis}: Narrative propagation modeling using transformer embeddings, PageRank centrality, and True Impact Score ($0.4 \times PR + 0.3 \times Engage + 0.3 \times Sim$) identifying high-risk campaigns with 25\% AUROC improvement
    \item Video forensics analysis, deepfake detection ensembles, audio-video synchronization verification, natural language processing, large language model-based content verification, and novel geopolitical context modeling
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The proposed system addresses critical gaps in existing research: general-purpose disinformation detectors demonstrate severe performance degradation (22--44\%) on military content due to domain shift, distinct visual characteristics (low-light drone imagery, occluded faces, compression artifacts), lack of geopolitical context awareness, and failure to model social media amplification dynamics. Our domain-specific architecture maintains \textbf{0.0\% degradation}, achieving \textbf{100\% accuracy on a curated 30-video armed conflict benchmark with 0\% false positive rate}, while the Social Media Impact module processes 69K tweets to detect coordinated campaigns during acceleration phase.

The technical innovation comprises a novel three-layer fusion strategy: \textbf{Layer 1} applies parameter-free geometric mean across forensic modules; \textbf{Layer 2} incorporates audio-video mismatch detection; \textbf{Layer 3} refines decisions using a verified decision tree with 93-feature Geopolitical Trust Score and social media propagation signals. The Image Authenticity module's 3-level ensemble (forensic $\rightarrow$ multimodal $\rightarrow$ reasoning) and Social Media module's directed influence networks provide explainable risk quantification essential for high-stakes military applications.

Comparative evaluation demonstrates 25--45 percentage point superiority over state-of-the-art baselines (VMID, COOLANT, GAME-ON, UniFD) on military content, with the integrated system providing real-time, zero-FP detection for defense, intelligence, and diplomatic applications. Extended validation confirms generalization across Operation Sindoor, India-China border incidents, and broader armed conflict corpora.

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\noindent \textbf{Keywords:} Multimodal Disinformation Detection, Armed Conflicts, Deepfake Detection, Image Authenticity Ensemble, Social Media Impact Analysis, Narrative Propagation, Geopolitical Intelligence, Hierarchical Fusion, PageRank Influence Modeling, True Impact Score, LLM Verification, Domain-Specific AI.

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{\small April 2026}
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