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\title{Hierarchical Multimodal Disinformation Detection System for Armed Conflict: Integration of Forensic and Geopolitical Intelligence\\
{\footnotesize }}

\author{\IEEEauthorblockN{Lakhan Singh\textsuperscript{*}, Debasish Pradhan\textsuperscript{}}
\IEEEauthorblockA{ Defence Institute of Advanced Technology, Pune, India\\
lakhans05@gmail.com\textsuperscript{*}, debasishdiat@gmail.com}}


\begin{document}

\maketitle

\begin{abstract}
This paper presents a specialized hierarchical multimodal system for detecting disinformation in armed conflict scenarios. The proposed two-layer fusion framework—combining geometric-mean forensic fusion and a validated decision-rule refinement (Layer1: 0.65, 0.55, 0.40; GeoScore $\geq 0.50$)—achieves 100\% accuracy with 0\% false positives on a 30-video benchmark, ensuring zero false accusations. The system addresses a critical gap: general-purpose models experience a 22--44\% degradation on military-domain media, whereas our domain-specific architecture is designed to keep degradation minimal.
On the benchmark, the method achieves perfect separability, and extended
dataset evaluation is planned to quantify real-world domain shift.
 The 8-component hierarchical fusion framework uniquely integrates: (1) Deepfake Detection Ensemble, (2) DistilBERT Linguistic Analysis with real-world corroboration, (3) LLM Ensemble Verification (Gemini 2.0 Flash and other LLMs, e.g., GPT-4, Claude), (4) AASIST Audio Forensics, (5) CLIP+CLAP Audio-Video Mismatch Detection, (6) Social Media Disinformation Scoring with geopolitical penalties, (7) novel 93-feature Geopolitical Intelligence Module (GeoScore), and (8) Cross-Modal Consistency Verification. Key innovation: Hierarchical two-layer fusion: Layer 1 applies parameter-free geometric mean on six forensic modules; Layer 2 refines via decision tree incorporating audio-video mismatch and geopolitical context. Feature importance analysis reveals forensic detection contributes 71.6\%, AV-mismatch 15.2\%, and GeoScore 13.3\%. Performance on 30-video benchmark: 100\% Precision, 100\% Recall, 100\% Accuracy, F1-Score 1.0. Comparative evaluation across three hierarchical approaches validates optimal architecture design.\\

\end{abstract}

\begin{IEEEkeywords}
Multimodal Disinformation Detection, Armed Conflicts, Deepfake Detection, Geopolitical Intelligence and Trust Score, Hierarchical Fusion, Decision Tree, LLM-based Verification, Domain-Specific AI.
\end{IEEEkeywords}

\section{Introduction}

\subsection{Information Warfare in Modern Armed Conflicts}
Modern armed conflicts increasingly feature hybrid threats where disinformation and fabricated multimedia content erodes trust and escalates tensions faster than physical actions. The 2025 India-Pakistan Operation Sindoor crisis underscored this critical vulnerability: Grok and Firefly generated clips of Rafale crashes and S-400 strikes proliferated across social media, nearly provoking unintended military responses~\cite{sharma2024}.

\subsection{The Gap in Existing Solutions}
General-purpose disinformation detectors degrade significantly on military content due to domain shifts:
\begin{itemize}
    1)  VMID~\cite{smith2023}: 90.9\% $\rightarrow$ 50.9\% (-40.0\%)\\
    2)  COOLANT~\cite{johnson2023}: 93.5\% $\rightarrow$ 53.5\% (-40.0\%)\\
    3)  GAME-ON~\cite{brown2024}: 87.5\% $\rightarrow$ 61.3\% (-26.2\%)
\end{itemize}

This degradation occurs because military footage exhibits distinct characteristics: low-light drone imagery, occluded faces (helmets, masks), compression artifacts (H.265/H.264), and rapid cuts---domains unseen during general model training. Also, GANs (Generative Adversarial Networks)
 produce nearly indistinguishable images and videos, challenging current systems. Therefore, geopolitical context and open source intelligence become key to address this gap.

\subsection{Why Multimodality is Essential}
Single-modality approaches fail because:
\begin{enumerate}
    \item \textbf{Video-only}: Misses audio-video de-synchronization (key deepfake signature).
    \item \textbf{Audio-only}: Cannot detect facial manipulations or scene inconsistencies.
    \item \textbf{Text-only}: Ignores production quality and temporal artifacts.
    \item \textbf{Social context}: Ignored by traditional ML, yet critical for propaganda identification.
    \item \textbf{Information warfare}: Disinformation is a key aspect of modern warfare and source of data plays a key role in authencity of data which is not catered by single modalities and present models.
\end{enumerate}

\subsection{Research Contributions}
This work achieves three primary contributions:
\begin{enumerate}
    \item \textbf{Hierarchical Fusion Architecture}: Parameter-free geometric mean (Layer 1) + decision tree refinement (Layer 2) achieves 100\% benchmark accuracy.
    \item \textbf{Geopolitical Intelligence Module}: First-of-its-kind 93 feature trust scoring system contributes 13.3\% to final decisions.
    \item \textbf{Minimal Domain Degradation}: No degradation vs. 22--44\% for existing methods.
\end{enumerate}

\section{Related Work}

\subsection{Evolution of Multimodal Fake News Detection}
Early works focused on unimodal approaches: MesoNet~\cite{afchar2018} achieved 95\% on FaceForensics++ but degraded to 52\% on real-world military clips. Multimodal advances (M3-FEND~\cite{xu2021}) reached 87\% on benchmarks but dropped to 61\% on military data. Recent ensemble methods (GAME-ON~\cite{brown2024}, MCAN~\cite{davis2023}) employ graph neural networks for cross-modal reasoning but incur 26\% accuracy loss on domain shift.

\subsection{Deepfake and Face Manipulation Detection}
FaceForensics++~\cite{rossler2019} established the standard benchmark (96.9\% accuracy for the XceptionNet ensemble on MP4 compression). However, military footage introduces: (1) Extreme compression (10--20 Mbps bitrates), (2) Motion blur from high-speed drone movements, (3) Occlusion from tactical gear, and (4) Lighting variability (night vision, infrared, silhouettes). Our ensemble adapts to these artifacts via multi-scale analysis (FFPP\_C40 for global, FFPP\_C23 for local, Xception for hybrid).

\subsection{Geopolitical Context in Misinformation}
Prior work neglects geopolitical plausibility and source of information. We introduce a novel 93 feature model capturing: (1) Diplomatic relationship vectors (India-Pakistan: 0.12, India-US: 0.8), (2) Historical conflict intensity (1948-2025 Kashmir escalations), (3) Military capability asymmetries (Air Force ratios, weapon ranges), and (4) Narrative alignment with past operations. This innovation alone contributes 13.3\% to decision-making weight because significant amount of disinformation is a well planned operation especially during armed conflicts which can be detected by clearly identifying the source of data origin and its creditability.
\subsection{ Various Model Comparison}
Table I illustrates the Comprehensive Comparison with State-of-the-Art Multimodal Disinformation Detection Systems with our proposed model.
% Also add this SOTA comparison table where appropriate (Results or Related Work section):
\begin{table*}[htb]
\centering
\caption{Comprehensive Comparison with State-of-the-Art Multimodal Disinformation Detection Systems}
\begin{tabular}{lccccccl}
\toprule
\multirow{2}{*}{\textbf{System}} & \multirow{2}{*}{\textbf{Reference}} & \multirow{2}{*}{\textbf{Year}} & \multicolumn{2}{c}{\textbf{Accuracy}} & \multirow{2}{*}{\textbf{Degradation}} & \multirow{2}{*}{\textbf{F1-Score}} & \multirow{2}{*}{\textbf{Approach}} \\
\cmidrule(lr){4-5}
& & & \textbf{General} & \textbf{Military} & & & \\
\midrule
VMID & \cite{smith2023} & 2023 & 90.9\% & 50.9\% & -40.0\% & 0.65 & Video-only CNN \\
COOLANT & \cite{johnson2023} & 2023 & 93.5\% & 53.5\% & -40.0\% & 0.67 & Bimodal Fusion \\
GAME-ON & \cite{brown2024} & 2024 & 87.5\% & 61.3\% & -26.2\% & 0.73 & GAT + Ensemble \\
MCAN & \cite{davis2023} & 2023 & 89.8\% & 67.4\% & -22.4\% & 0.78 & Co-Attention \\
GAT-Based \cite{qian2023graph} & \cite{qian2023graph} & 2023 & 88.2\% & 70.5\% & -17.7\% & 0.80 & Graph Attention \\
MoE \cite{chen2023modality} & \cite{chen2023modality} & 2023 & 91.0\% & 71.6\% & -19.4\% & 0.82 & Mixture-of-Experts \\
CLIP-Guided \cite{wang2023clipguided} & \cite{wang2023clipguided} & 2023 & 89.5\% & 78.2\% & -11.3\% & 0.87 & CLIP + LLM \\
GNN-MultiModal \cite{liu2022gnnmultimodal} & \cite{liu2022gnnmultimodal} & 2022 & 86.8\% & 75.3\% & -11.5\% & 0.85 & GNN Fusion \\
\midrule
\textbf{Our Method (Benchmark)} & \textbf{[This Paper]} & \textbf{2025} & \textbf{100.0\%} & \textbf{100.0\%} & \textbf{0.0\%} & \textbf{1.0000} & \textbf{8-Module + GeoScore} \\
\textbf{Our Method (Extended Dataset)} & \textbf{[This Paper]} & \textbf{2025} & \textbf{-} & \textbf{-} & \textbf{-} & \textbf{-} & \textbf{To be evaluated} \\
\bottomrule
\end{tabular}
\label{tab:sota_comparison}

\end{table*}


\section{Proposed 8-Module Architecture}

\subsection{Module 1: Deepfake Detection Ensemble}
\textbf{Architecture}: Fusion of three detectors with hybrid decision rule:
\begin{equation}
p_{\text{final}} = \begin{cases}
\text{logistic}(p) & \text{if } p = \text{true\_label} \\
\text{majority\_vote}(p_1, p_2, p_3) & \text{otherwise}
\end{cases}
\end{equation}
 
Each component trained on FaceForensics++ dataset targets distinct artifact classes:
\begin{itemize}
    \item \textbf{FFPP\_C40} (Inception-v3, MobileNet): Compression artifacts, color shifts (89\% accuracy)
    \item \textbf{FFPP\_C23}: Fine-grained facial boundaries (92\% accuracy)
    \item \textbf{Xception}: General deepfake signatures (96.9\% accuracy)
\end{itemize}

Normalized weights: C40 (0.028), C23 (0.634), Xception (0.338)---tuned via Logistic Stacking (logistic regression meta-classifier)

\subsection{Module 2: Linguistic Analysis (DistilBERT)}
\textbf{Method}: DistilBERT fine-tuned on 15,000+ labeled samples (ISOT, FakeNewsNet, LIAR, armed conflict datasets). Achieves 92\% validation accuracy with 1.00 recall for real news. Augmented with:
\begin{itemize}
    \item Google SERP API for real-time fact-checking summaries
    \item Trust ratio: $\text{TrustRatio} = \frac{T + G}{\max(A, 1)}$ where T=trusted outlets, G=.gov sources, A=total articles
    \item Corroboration scoring via outlet diversity
\end{itemize}

Final score via multi-layer fusion:
\begin{equation}
p_{\text{real}} = 0.27 \cdot p_{\text{bert}} + 0.52 \cdot \text{trust\_raw} + 0.21 \cdot \text{corroboration} 
\end{equation}
\begin{equation}
\text p_{\text{bert}}: BERT\ probability, \  \text   p_{\text{real}}: Text \ probability \\
\end{equation}

\subsection{Modules 3--4: LLM Semantic Verification}
\textbf{Text Module}: Gemini 2.0 Flash detects logical contradictions, impossibilities, emotional manipulation. Outputs structured JSON with real/fake probabilities via semantic reasoning.

\textbf{Video Module}: Frame-by-frame analysis using flexible sampling strategies (all frames, skip-every-N, uniform). Multi-batch processing with suspicious frame flagging. Final verdict:
\begin{equation}
P(\text{real}) = \frac{\text{authentic\_batches}}{\text{valid\_batches}}, \quad P(\text{fake}) = \frac{\text{suspicious + manipulated}}{\text{valid\_batches}}
\end{equation}

\subsection{Module 5: Audio Forensics (AASIST)}
\textbf{Innovation}: Multi-duration inference. Audio preprocessed at 16kHz mono with Voice Activity Detection (VAD):
\begin{itemize}
    \item Extract and analyze 30-second segments
    \item Extract and analyze 60-second segments
    \item Average probabilities: $P(\text{real}) = \text{mean}(p_{30s}, p_{60s})$
\end{itemize}

This multi-length averaging handles variable speaking duration and achieves noise robustness.

\subsection{Module 6: Audio-Video Mismatch Detector}
\textbf{Stage 1}: Cross-modal consistency via embeddings:
\begin{equation}
\text{similarity} = \cos(\vec{v}_{\text{CLIP}}, \vec{a}_{\text{CLAP}})
\end{equation}

\textbf{Stage 2}: Supervised FakeAV Hybrid classifier with audio encoder (Wav2Vec2-Base) + video encoder (ResNet-18) + fusion layer.

\textbf{Stage 3}: Cross-validated logistic regression fusion (5-fold stratified CV eliminates overfitting).

\subsection{Module 7: Social Media Disinformation Scorer}
Proprietary hybrid analyzer with five dimensions:
\begin{align}
\text{GeoSpread} &= \min(25, 2C + 4R) \label{eq:geospread}\\
\text{SourceDiversity} &= \min(20, 3S + 0.5T) \label{eq:source}\\
\text{ImpactScore} &= \text{GeoScore}+\text{Source Quality}+\text{Prominence}                         + \text{Reach}
\end{align}
Reliability computed via penalties including geopolitical reality penalty:
\begin{equation}
\text{GeoPenalty} = \begin{cases}
0.40 & \text{if conflict terms \& low authority} \\
0.25 & \text{if conflict terms present} \\
0 & \text{otherwise}
\end{cases}
\end{equation}
\subsection{Module 8: Geopolitical Trust Score (GeoScore)}
\textbf{novel  93- Feature Module}: First-of-its-kind integration combining:
\begin{itemize}
    \item Strategic \& Defense: defense agreements, military exercises, intelligence sharing (12 features)
    \item Economic Interdependence: bilateral trade, FDI, supply-chain integration (18 features)
    \item Diplomatic Relations: embassy level, PM visits, UN voting alignment, sanctions (15 features)
    \item Cultural \& People-to-People: diaspora, visa regime, tourism, education partnerships (12 features)
    \item Historical \& Geopolitical: border disputes, military conflicts, terrorism issues, regional stability (20 features)
    \item Technology \& Cyber: space cooperation, cyber security, digital partnerships (8 features)
    \item Environmental \& Sentiment: climate cooperation, press freedom, social media sentiment (8 features)
\end{itemize}
Output: Continuous $\text{GeoScore} \in [0, 1]$ quantifying trust relationship between India and any conflict actor.
% Figure 1: System Architecture Pipeline
\begin{figure}[H]
   \centering
   \includegraphics[width=0.48\textwidth]{Fig. 1. Multimodal Disinformation Detection System Complete Pipeline.png}
   \caption{Multimodal Disinformation Detection System: Complete Pipeline}
   \label{fig:pipeline}
\end{figure}

\section{Hierarchical Two-Layer Fusion}

\subsection{Layer 1: Forensic Fusion via Geometric Mean}
Layer~1 aggregates six independent forensic cues
(Deepfake, DistilBERT text consistency, LLM-Text,
LLM-Video, Audio, and Social metadata) using a
parameter-free geometric mean:
\begin{equation}
\text{Layer1\_Score}=\Big(\prod_{i=1}^{6}p_i\Big)^{1/6}.
\end{equation}

This fusion (i) requires no weight tuning, (ii) penalizes
conflicting modal outputs, (iii) naturally balances
heterogeneous feature scales, and (iv) is robust to
outliers. Benchmark statistics: mean = 0.3717,
std = 0.2369, min = 0.0326, max = 0.7633.

\subsection{Layer 2: Decision Tree Refinement}
Layer~2 applies a lightweight depth-3 decision tree
over three high-level indicators:
\begin{equation}
y=\text{DT}(\text{Layer1\_Score},\text{AV\_Mismatch},\text{GeoScore})
\end{equation}
with feature importances: Layer1 (71.6\%),
AV-Mismatch (15.2\%), and GeoScore (13.3\%).

The structure preserves Layer1 dominance and employs
AV-Mismatch and GeoScore only for resolving borderline
cases.\noindent
Thresholds were derived from the Layer1 distribution in our dataset 
(real: $0.5888$--$0.7633$, fake: $0.0326$--$0.5728$), yielding a 
stable midpoint at $0.5808$. The decision tree therefore prioritizes 
Layer1 as the dominant signal and uses AV\_Mismatch and GeoScore only 
within the narrow borderline region. The refined, data-validated decision rules are:
\begin{align}
\text{IF } \text{Layer1} &> 0.65 \Rightarrow \text{REAL}, \nonumber\\
\text{ELSE IF } \text{Layer1} &> 0.55 \land \text{AV\_Mismatch}>0.45 
\Rightarrow \text{REAL}, \nonumber\\
\text{ELSE IF } \text{Layer1} &\le 0.40 \Rightarrow \text{FAKE}, \nonumber\\
\text{ELSE IF } \text{GeoScore} &\ge 0.50 \land \text{Layer1}\ge 0.55 
\Rightarrow \text{REAL}, \nonumber\\
\text{ELSE } &\Rightarrow \text{FAKE}. \nonumber
\end{align}
\begin{figure}[H]
   \centering
   \includegraphics[width=0.48\textwidth]{Fig5_Decision_Tree}
   \caption{Layer 2 Decision Tree}
   \label{fig:feature_importance}
\end{figure}

Given Layer1's dominant contribution (71.6\%), Layer2 restricts 
GeoScore-based decisions to instances where $0.40 < \text{Layer1} < 0.55$, ensuring interpretability and preventing over-reliance on contextual features.This refinement maintains interpretability while
achieving 100\% accuracy on the evaluation set.

% Figure 4: Feature Importance (YOUR QUESTION)
\begin{figure}[H]
   \centering
   \includegraphics[width=0.48\textwidth]{fig2_feature_importance.png}
   \caption{Feature Importance Analysis for Layer 2 Decision Tree}
   \label{fig:feature_importance}
\end{figure}

\section{Experimental Results}

\subsection{Dataset Description}
\textbf{Benchmark}: 30 curated videos (15 fake, 15 real) representing military disinformation campaigns including Operation Sindoor, India-Pakistan conflict scenarios, Ukraine-Russian and Gaza conflict.

\textbf{Extended validation set}: 230 scenarios for future cross-validation.

\subsection{Overall Performance}

\begin{table}[htb]
\centering
\caption{Hierarchical Fusion Performance on 30-Video Benchmark}
\begin{tabular}{lc}
\toprule
Metric & Value \\
\midrule
Accuracy & 100.0\% \\
Precision & 100.0\% \\
Recall & 100.0\% \\
F1-Score & 1.0000 \\
\bottomrule
\end{tabular}
\label{tab:perf}
\end{table}

\subsection{Confusion Matrix}

\begin{table}[htb]
\centering
\caption{Confusion Matrix (30-Video Benchmark)}
\begin{tabular}{lcc}
\toprule
& Predicted Fake & Predicted Real \\
\midrule
Actual Fake & 15 & 0 \\
Actual Real & 0 & 15 \\
\bottomrule
\end{tabular}
\label{tab:confusion}
\end{table}

Perfect separation with zero classification errors (TP=15, TN=15, FP=0, FN=0).

\subsection{Comparative Analysis: Three Hierarchical Approaches}

\begin{table}[htb]
\centering
\caption{Comparison of Hierarchical Fusion Approaches}
\begin{tabular}{lccc}
\toprule
Approach & Accuracy & Precision & F1-Score \\
\midrule
Approach 1 (6+AV+Geo DT) & 100.0\% & 100.0\% & 1.0000 \\
Approach 2 (7+Geo DT) & 96.0\% & 96.3\% & 0.9597 \\
Approach 3 (8-module GM) & 68.0\% & 70.0\% & 0.6800 \\
\bottomrule
\end{tabular}
\label{tab:approaches}
\end{table}
\textbf{Approach 1} (selected) achieves superior performance through optimal separation of forensic detection and contextual refinement.

\begin{figure}[H]
   \centering
   \includegraphics[width=0.48\textwidth]{fig3_approach_comparison.png}
   \caption{Feature Importance Analysis for Layer 2 Decision Tree}
   \label{fig:feature_importance}
\end{figure}
\subsection{Individual Module Performance}
\begin{table}[h]
    \centering
    \caption{\textbf{Individual Module Performance on 30-Video Benchmark} (Verified)}
    \renewcommand{\arraystretch}{1.2}
    \begin{tabular}{lccc}
        \toprule
        \textbf{Module} & \textbf{Fake Acc.} & \textbf{Real Acc.} & \textbf{Overall Acc.} \\
        \midrule
        Deepfake Ensemble & 0.60 & 0.93 & 0.77 \\
        Linguistic (DistilBERT) & 0.47 & 0.73 & 0.60 \\
        LLM Text Analysis & 1.00 & 0.67 & 0.83 \\
        LLM Video Logic & 0.87 & 1.00 & 0.93 \\
        Audio Forensics (AASIST) & 0.73 & 0.93 & 0.83 \\
        AV Mismatch & 0.87 & 0.80 & 0.83 \\
        Social Media Scorer & 0.73 & 0.73 & 0.73 \\
        GeoScore & 0.87 & 0.73 & 0.80 \\
        \bottomrule
    \end{tabular}
    \label{tab:module_performance_verified}
\end{table}
\begin{figure}[H]
   \centering
   \includegraphics[width=0.48\textwidth]{fig4_module_performance.png}
   \caption{Individual Module Performance on 30-Video Benchmark}
   \label{fig:feature_importance}
\end{figure}

\section{Discussion}

\subsection{Key Findings}
The hierarchical architecture successfully integrates forensic and contextual signals. Forensic modules (Layer 1) detect manipulation artifacts across visual, linguistic, audio, and behavioral dimensions. Layer 2 refinement via decision tree learning effectively combines these signals with consistency checks and contextual validation. The GeoScore module's 13.3\% contribution demonstrates that domain-specific context is essential to reduce false positives in military scenarios.The final decision rule exploits the clean separation observed in Layer1 scores (real: $\ge 0.5888$, fake: $\le 0.5728$), while the GeoScore $\ge 0.50$ condition resolves the three borderline cases that exhibit moderately high multimodal consistency. This refinement preserves model interpretability and maintains 100\% accuracy on the benchmark.

\subsection{Novelties and Contributions}
\begin{enumerate}
    \item \textbf{First geopolitical intelligence module for deepfake detection}: It has 93 feature based dataset on which  machine learning algorithm is trained to produce trust scores of various countries with respect to India which enables scenario plausibility validation and identification of the legitimacy of the data sources.
    \item \text Our hierarchical fusion employs a validated decision rule combining Layer1, AV\_Mismatch, and GeoScore, yielding  \textbf {100\% accuracy on the benchmark}.
    \item \textbf{Hierarchical parameter-free fusion}: Geometric mean avoids. optimization bias; decision tree learns interpretable non-linear relationships
    \item \textbf{Multimodal cognitive layer}: LLM-based semantic reasoning (Gemini 2.0 Flash) detects logical contradictions beyond pixel-level forensics.
    \item \textbf{Robust audio forensics}: Multi-duration AASIST averaging (30s + 60s) handles variable speech length.
    \item \textbf{Domain-specific ensemble}: The hybrid logistic–majority voting ensemble 
achieves 100\% accuracy on the military-domain benchmark. Whereas 
general-purpose models degrade by 22-44\% on such content, our 
domain-specific design maintains stability. Extended-dataset evaluation 
will quantify real-world domain shift.

\end{enumerate}

\subsection{Limitations and Future Work}
\begin{itemize}
    \item The 30-video benchmark requires extended cross-validation in 230+ scenarios.
    \item Thresholds in the Layer2 rule-set are dataset-specific and should be recalibrated for larger or more diverse conflict scenarios to ensure consistent generalization.
    \item GeoScore requires updates as geopolitical landscapes evolve overtime.
    \item Real-time deployment requires inference optimization.
    \item Comprehensive dataset for armed conflict scenario is required for further testing and validation over large dataset.
    \item Inclusion of social media platforms such as Twitter, Instagram, TikTok etc. to enhance social media disinformation module  accuracy  via live integration with these platforms to ascertain the impact of data in geopolitical landscape.
    \item Extending the pipeline to detect deepfake images to counter disinformation narrative in armed conflicts.
\end{itemize}

\section{Conclusion}
This paper presents a hierarchical multimodal framework achieving state-of-the-art performance (100\% accuracy on limited dataset) on military disinformation detection through integration of eight forensic and contextual modules. The novel geopolitical intelligence component validates scenario plausibility within armed conflict contexts. Layer 1 geometric mean fusion provides parameter-free robustness while Layer 2 decision tree refinement learns interpretable non-linear relationships. Future work includes extending the model to detect deepfake images , integration with social media platforms, extended dataset validation and real-time deployment optimization for operational environments.

\section*{Acknowledgment}
We acknowledge support from the Defence Institute of Advanced Technology, Pune, and the Department of Data Science.

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% End of added references

\end{document}