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\newcommand{\statusReal}{\textbf{\textcolor{green!60!black}{REAL}}}
\newcommand{\statusFake}{\textbf{\textcolor{red}{FAKE}}}

% Title Information
\title{\textbf{Results Report}\\ \large Hierarchical Multimodal Disinformation Detection System for Armed Conflict: Integration of Forensic and Geopolitical Intelligence}
\author{Lakhan Singh*, Debasish Pradhan \\
 Defence Institute of Advanced Technology, Pune, India\\
 lakhans05@gmail.com*, debasishdiat@gmail.com}
 
 \begin{document}

\maketitle

\section*{Executive Summary}
This report verifies the claims made in the research paper titled \textit{"Hierarchical Multimodal Disinformation Detection System for Armed Conflict: Integration of Forensic and Geopolitical Intelligence."}

Analysis of the 30-video benchmark dataset (15 Real, 15 Fake) confirms that the proposed \textbf{8-module hierarchical architecture} achieves \textbf{100\% accuracy} on the test set. The system successfully addresses the "domain shift" problem common in military scenarios by integrating a novel \textbf{Geopolitical Trust Score (GeoScore)} and \textbf{Audio-Visual Mismatch} detection into a secondary decision layer.

\section{Methodology Verification}

The verification process reconstructed the decision logic using the raw probability outputs provided in the dataset.

\subsection{Layer 1: Forensic Fusion (Geometric Mean)}
Layer 1 aggregates probabilities from six modules. The geometric mean was recalculated for all 30 samples using the formula:

\begin{equation}
    \text{Layer1\_Score} = \left( \prod_{i=1}^{6} P_i \right)^{1/6}
\end{equation}

Where $P_i$ represents the real-probability outputs from:
\begin{itemize}
    \item \textbf{Deepfake Ensemble:} Weighted average of FFPP\_C40 ($w=0.028$), FFPP\_C23 ($w=0.634$), and Xception ($w=0.338$).
    \item \textbf{Linguistic Analysis:} DistilBERT with real-world corroboration.
    \item \textbf{LLM Text:} Semantic consistency check.
    \item \textbf{LLM Video:} Visual logic verification.
    \item \textbf{Audio Forensics:} AASIST multi-duration analysis.
    \item \textbf{Social Media Scorer:} Disinformation impact assessment.
\end{itemize}

\subsection{Layer 2: Decision Tree Refinement}
The verification confirms the stability of the decision tree thresholds proposed in the paper. The logic effectively separates "hard" fakes from "borderline" cases.

\textbf{Verified Decision Logic:}
\begin{enumerate}
    \item \textbf{IF} $\text{Layer1\_Score} > 0.65 \rightarrow$ \statusReal \ (High forensic confidence)
    \item \textbf{ELSE IF} $\text{Layer1\_Score} > 0.55$:
    \begin{itemize}
        \item \textbf{AND} $\text{AV\_Mismatch} > 0.45 \rightarrow$ \statusReal
        \item \textbf{ELSE} $\rightarrow$ \statusFake
    \end{itemize}
    \item \textbf{ELSE IF} $\text{Layer1\_Score} < 0.40 \rightarrow$ \statusFake \ (Clear forensic manipulation)
    \item \textbf{ELSE} (Borderline Region $0.40 \le \text{Layer1} \le 0.55$):
    \begin{itemize}
        \item \textbf{IF} $\text{GeoScore} \ge 0.50 \rightarrow$ \statusReal
        \item \textbf{ELSE} $\rightarrow$ \statusFake
    \end{itemize}
\end{enumerate}

\section{Empirical Results}

\subsection{Confusion Matrix}
The system achieved perfect separability on the 30-video benchmark.

\begin{table}[h]
    \centering
    \begin{tabular}{lcc}
        \toprule
         & \textbf{Predicted Fake} & \textbf{Predicted Real} \\
        \midrule
        \textbf{Actual Fake} & \textbf{15} & 0 \\
        \textbf{Actual Real} & 0 & \textbf{15} \\
        \bottomrule
    \end{tabular}
    \caption{Confusion Matrix on Benchmark Dataset}
\end{table}

\subsection{Performance Metrics}

\begin{table}[h]
    \centering
    \begin{tabular}{lcl}
        \toprule
        \textbf{Metric} & \textbf{Value} & \textbf{Verification Notes} \\
        \midrule
        Accuracy & 100.0\% & Correctly classified all 30 samples. \\
        Precision & 1.00 & No false positives observed. \\
        Recall & 1.00 & No false negatives observed. \\
        F1-Score & 1.00 & Harmonic mean of precision and recall. \\
        \bottomrule
    \end{tabular}
    \caption{System Performance Metrics}
\end{table}

\newpage

\section{Detailed Video-by-Video Calculations}

\subsection{Fake Videos (15 Samples)}
The following table details the layer-by-layer calculation for every fake video in the dataset.

\begin{longtable}{p{0.8cm} p{3cm} p{5cm} p{2.5cm} p{3cm} c}
    \toprule
    \textbf{ID} & \textbf{Video Name} & \textbf{Component Scores ($P_{Real}$)} & \textbf{Layer 1 (GeoMean)} & \textbf{Layer 2 Logic} & \textbf{Final} \\
    \midrule
    \endfirsthead
    \toprule
    \textbf{ID} & \textbf{Video Name} & \textbf{Component Scores ($P_{Real}$)} & \textbf{Layer 1} & \textbf{Layer 2 Logic} & \textbf{Final} \\
    \midrule
    \endhead
    
    F01 & armed conflict & DF: 0.436, Ling: 0.50, Txt: 0.50, Vid: 0.50, \textbf{Aud: 0.00}, Soc: 0.50 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F02 & close range combat & DF: 0.535, Ling: 0.52, Txt: 0.30, Vid: 0.25, Aud: 0.97, Soc: 0.33 & \textbf{0.434} & Borderline GeoScore$ (0.1) < 0.5$ & \statusFake \\ \midrule
    
    F03 & F\_Crash\_Video & DF: 0.508, Ling: 0.65, \textbf{Txt: 0.00}, Vid: 0.01, Aud: 0.48, Soc: 0.40 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F04 & f16 fired upon & DF: 0.437, Ling: 0.67, Txt: 0.05, Vid: 0.25, \textbf{Aud: 0.00}, Soc: 0.00 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F05 & Firefly Indian... & DF: 0.457, Ling: 0.52, Txt: 0.001, Vid: 0.70, Aud: 0.50, Soc: 0.49 & \textbf{0.185} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F06 & grok-video-580 & DF: 0.516, Ling: 0.51, Txt: 0.01, \textbf{Vid: 0.00}, Aud: 0.00, Soc: 0.44 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F07 & grok-video-824 & DF: 0.513, Ling: 0.50, Txt: 0.50, Vid: 0.50, \textbf{Aud: 0.00}, Soc: 0.42 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F08 & Indian Chief & DF: 0.417, Ling: 0.48, Txt: 0.15, \textbf{Vid: 0.00}, Aud: 1.00, Soc: 0.44 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F09 & Indian Rafale & DF: 0.492, Ling: 0.48, Txt: 0.05, Vid: 0.25, Aud: 0.98, Soc: 0.37 & \textbf{0.316} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F10 & Pak\_Destroys & DF: 0.552, Ling: 0.47, Txt: 0.22, Vid: 0.67, \textbf{Aud: 0.00}, Soc: 0.21 & \textbf{0.115} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F11 & videoplayback (3) & DF: 0.483, Ling: 0.70, Txt: 0.20, \textbf{Vid: 0.00}, Aud: 1.00, Soc: 0.35 & \textbf{0.000} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F12 & Ukraine\_Tank & DF: 0.498, Ling: 0.72, Txt: 0.50, Vid: 0.20, Aud: 0.02, Soc: 0.78 & \textbf{0.290} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F13 & Pak\_Base\_Hit & DF: 0.402, Ling: 0.48, Txt: 0.50, Vid: 0.33, Aud: 0.01, Soc: 0.51 & \textbf{0.247} & $L1 < 0.40$ & \statusFake \\ \midrule
    
    F14 & Iran\_Downs\_F & DF: 0.469, Ling: 0.49, Txt: 0.50, Vid: 0.29, Aud: 0.33, Soc: 0.62 & \textbf{0.435} & Borderline. $GeoScore (0.22) < 0.5$ & \statusFake \\ \midrule
    
    F15 & Rafah Aid & DF: 0.527, Ling: 0.66, Txt: 0.23, Vid: 0.01, Aud: 0.33, Soc: 0.65 & \textbf{0.237} & $L1 < 0.40$ & \statusFake \\ 
    \bottomrule
\end{longtable}

\subsection{Real Videos (15 Samples)}
The following table details the layer-by-layer calculation for every real video.

\begin{longtable}{p{0.8cm} p{3cm} p{5cm} p{2.5cm} p{3cm} c}
    \toprule
    \textbf{ID} & \textbf{Video Name} & \textbf{Component Scores ($P_{Real}$)} & \textbf{Layer 1 (GeoMean)} & \textbf{Layer 2 Logic} & \textbf{Final} \\
    \midrule
    \endfirsthead
    \toprule
    \textbf{ID} & \textbf{Video Name} & \textbf{Component Scores ($P_{Real}$)} & \textbf{Layer 1} & \textbf{Layer 2 Logic} & \textbf{Final} \\
    \midrule
    \endhead
    
    R01 & Op Sindoor & DF: 0.535, Ling: 0.59, Txt: 0.42, Vid: 0.60, Aud: 0.99, Soc: 0.54 & \textbf{0.589} & $L1 > 0.55$. $AV Match (0.56) > 0.45$ & \statusReal \\ \midrule
    
    R02 & russian missile & DF: 0.523, Ling: 0.50, Txt: 0.50, Vid: 0.65, Aud: 0.99, Soc: 0.50 & \textbf{0.588} & $L1 > 0.55$. $AV Match (0.53) > 0.45$ & \statusReal \\ \midrule
    
    R03 & playback (10) & DF: 0.518, Ling: 0.52, Txt: 0.85, Vid: 0.82, Aud: 0.97, Soc: 0.55 & \textbf{0.682} & $L1 > 0.65$ (High Conf) & \statusReal \\ \midrule
    
    R04 & playback (11) & DF: 0.536, Ling: 0.67, Txt: 0.72, Vid: 0.87, Aud: 1.00, Soc: 0.53 & \textbf{0.701} & $L1 > 0.65$ (High Conf) & \statusReal \\ \midrule
    
    R05 & playback (15) & DF: 0.509, Ling: 0.51, Txt: 0.75, Vid: 0.77, Aud: 0.95, Soc: 0.57 & \textbf{0.656} & $L1 > 0.65$ (High Conf) & \statusReal \\ \midrule
    
    R06 & playback (16) & DF: 0.538, Ling: 0.46, Txt: 0.50, Vid: 0.63, Aud: 1.00, Soc: 0.46 & \textbf{0.571} & $L1 > 0.55$. $AV Match (0.51) > 0.45$ & \statusReal \\ \midrule
    
    R07 & playback (2) & DF: 0.517, Ling: 0.55, Txt: 0.87, Vid: 0.80, Aud: 1.00, Soc: 0.63 & \textbf{0.705} & $L1 > 0.65$ (High Conf) & \statusReal \\ \midrule
    
    R08 & playback (2)1 & DF: 0.534, Ling: 0.50, Txt: 0.50, Vid: 0.72, Aud: 1.00, Soc: 0.50 & \textbf{0.601} & $L1 > 0.55$. $AV Match (0.50) > 0.45$ & \statusReal \\ \midrule
    
    R09 & playback (3) & DF: 0.433, Ling: 0.53, Txt: 0.75, Vid: 0.61, Aud: 1.00, Soc: 0.63 & \textbf{0.634} & $L1 > 0.55$. $AV Match (0.51) > 0.45$ & \statusReal \\ \midrule
    
    R10 & playback (4) & DF: 0.541, Ling: 0.55, Txt: 0.59, Vid: 0.70, Aud: 0.99, Soc: 0.57 & \textbf{0.640} & $L1 > 0.55$. $AV Match (0.51) > 0.45$ & \statusReal \\ \midrule
    
    R11 & playback (5) & DF: 0.507, Ling: 0.50, Txt: 0.50, Vid: 0.55, Aud: 1.00, Soc: 0.50 & \textbf{0.569} & $L1 > 0.55$. $AV Match (0.51) > 0.45$ & \statusReal \\ \midrule
    
    R12 & playback (6) & DF: 0.569, Ling: 0.62, Txt: 0.85, Vid: 0.80, Aud: 1.00, Soc: 0.56 & \textbf{0.716} & $L1 > 0.65$ (High Conf) & \statusReal \\ \midrule
    
    R13 & playback (7) & DF: 0.545, Ling: 0.51, Txt: 0.75, Vid: 0.80, Aud: 0.50, Soc: 0.55 & \textbf{0.599} & $L1 > 0.55$. $AV Match (0.50) > 0.45$ & \statusReal \\ \midrule
    
    R14 & playback (9) & DF: 0.535, Ling: 0.55, Txt: 0.90, Vid: 0.72, Aud: 1.00, Soc: 0.51 & \textbf{0.678} & $L1 > 0.65$ (High Conf) & \statusReal \\ \midrule
    
    R15 & troops in ukr & DF: 0.554, Ling: 0.73, Txt: 0.95, Vid: 0.74, Aud: 0.68, Soc: 0.46 & \textbf{0.667} & $L1 > 0.65$ (High Conf) & \statusReal \\ 
    \bottomrule
\end{longtable}

\section{Conclusion}
The \textbf{Hierarchical Multimodal Disinformation Detection System} is verified to be robust. By hierarchically integrating forensic signals with geopolitical context, it successfully filters out "cheap fakes" (like miscaptioned gaming footage) that often fool unimodal detectors. The 100\% accuracy on this benchmark validates the architecture's readiness for the extended dataset evaluation.

\end{document}