\section{Introduction}
\label{sec:intro}
\begin{figure}[t]
    \centering
    \begin{subfigure}{\linewidth}
        \centering
        \includegraphics[width=\linewidth]{sec/intro_compar.jpg}
        \caption{Conventional frequency-based Deepfake Identification}
    \end{subfigure}

    \textbf{\\}\begin{subfigure}{\linewidth}
        \centering
        \includegraphics[width=\linewidth]{sec/intro_compar_.jpg}
        \caption{WaveDIF: Wavelet energy-based Deepfake Identification}
    \end{subfigure}
    \caption{\label{fig:intro_compar} WaveDIF: Wavelet sub-band based Deepfake Identification in Frequency Domain. (a) Traditionally, frequency level artifacts (like DFT, DCT, FFT, etc.) are generated, which in image format are fed to Convolutional Neural Networks (CNNs) for feature extraction, and then classification. (b) WaveDIF filters out high frequency artifacts using a low-pass gaussian filter and uses DWT to decompose videos into sub-bands LL, LH, HL, and HH. Further, the energy level values of these sub-bands are used as input features for classification.} 
\end{figure}

















\begin{figure*}[]
    \centering
    \makebox[0.16\linewidth]{Original}
    \makebox[0.16\linewidth]{\textsc{Deepfake}}
    \makebox[0.16\linewidth]{\textsc{Face2Face}}
    \makebox[0.16\linewidth]{\textsc{FaceShifter}}
    \makebox[0.16\linewidth]{\textsc{FaceSwap}}
    \makebox[0.16\linewidth]{\textsc{NeuralTextures}}
    
    \textbf{\\}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/images_folder/original_intro.jpg}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/images_folder/deepfake_intro.jpg}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/images_folder/face2face_intro.jpg}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/images_folder/faceshifter_intro.jpg}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/images_folder/faceswap_intro.jpg}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/images_folder/neuraltextures_intro.jpg}

    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_outputs/original_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_outputs/deepfake_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_outputs/face2face_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_outputs/faceshifter_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_outputs/faceswap_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_outputs/neuraltextures_intro.png}

    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_difference_masks/original_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_difference_masks/deepfake_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_difference_masks/face2face_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_difference_masks/faceshifter_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_difference_masks/faceswap_intro.png}
    \includegraphics[width=0.16\linewidth]{sec/ff_intro/dft_difference_masks/neuraltextures_intro.png}

    \caption{\label{fig:face_forensics_intro} A sample video from the \texttt{FaceForensics++} \cite{rossler2019faceforensics++} dataset (first row). Corresponding to the selected sample, five deepfakes – \textsc{Deepfakes} \cite{kaur2020deepfakes}, \textsc{Face2Face} \cite{thies2019face2face}, \textsc{FaceShifter} \cite{li2019faceshifter}, \textsc{FaceSwap} \cite{nirkin2019fsgan}, and \textsc{Neural Textures} \cite{thies2019deferred} are generated. The second row shows the Discrete Fourier transformation of the frames. Though difficult to visualize, the DFTs differ from each other considerably; SSIM (Original, \textsc{Deepfakes}) = 0.6028, SSIM (Original, \textsc{Face2Face}) = 0.6010, SSIM (Original, \textsc{FaceShifter}) = 0.5811, SSIM (Original, \textsc{FaceSwap}) = 0.5823, SSIM (Original, \textsc{Neural Textures}) = 0.5991. The last row shows the difference heatmap where bright red or yellow areas denotes regions of major modifications, in green or blue are regions of moderate to low modifications.}
\end{figure*}

Deepfakes are artificially generated videos in which the facial expression or contours of any source is replaced or transformed or concatenated with that of a target subject \cite{nguyen2022deep, zhang2020deep,fernando2025face,pei2024deepfake, masood2023deepfakes}. Deepfakes, in recent times are becoming very realistic, and thus can have severe negative societal impact \cite{hancock2021social, al2023impact}, and thus necessitates the need for having detection mechanism to identify such manipulated media. 

% \color{magenta}
In recent years, deepfake research has taken into consideration two different domains of operation (primarily) -- the \textbf{spatial} domain \cite{naskar2024deepfake, agarwal2021md, das2023unmasking, he2024gazeforensics}, which involves pixel-based manipulations, facial landmarks, and texture synthesis, and the \textbf{frequency} domain \cite{tan2024frequency, kohli2021detecting, jeong2022frepgan, hasanaath2025fsbi}, which involves signal transformations, frequency artifacts, and inconsistencies in high and low-frequency details. Additionally, some works \cite{astrid2025audio, usmani2025spatio, yang2023avoid} have suggested multi-modal deepfake detection, where features from more than one domain are combined and fused to form yet another complex feature set, based on which the video is classified as original or deepfake. 



% Deepfakes primarily have feature components from three domains – \textbf{spatial} \cite{naskar2024deepfake, agarwal2021md, das2023unmasking, he2024gazeforensics}, which involves pixel-based manipulations, facial landmarks, and texture synthesis; \textbf{frequency}  \cite{tan2024frequency, kohli2021detecting, jeong2022frepgan, hasanaath2025fsbi}, which involves signal transformations, frequency artifacts, and inconsistencies in high and low-frequency details; and \textbf{audio} \cite{hamza2022deepfake, warren2025pitch, elavarasi2025detecting, wani2025audio}, which involves voice synthesis, speech modulation, and lip-synchronization. 



% Figure \ref{fig:faceswap_intro} shows an example of Deepfake, generated through \textsc{FaceSwap} \cite{ghasemzadeh2024towards} where the face mask from the source is concatenated on the target.








Traditionally, features from all (or a set of) domains are feed to deep learning modules which learns domain-specific artifacts, therefore enabling a classification. While many works from the recent literature considers spatial domain for deepfake detection, comparatively fewer contributions have been made towards deepfake detection strictly pertaining to the frequency domain. This research presents WaveDIF, a strict frequency domain, lightweight deepfake video detection algorithm using wavelet sub-band energies (LL, LH, HL, and HH are the sub bands). Every deepfake detection framework usually works in two phases -- (a) \textit{feature extraction}, wherein features particular to original and deepfake videos are learned, and (b) \textit{classification}, wherein based on the learned feature a decision boundary is laid between deepfake and original videos \cite{fernando2025face}. Conventional deepfake detection frameworks mostly rely on convolutional neural networks (CNNs) for the feature 
extraction process, and a fully-connected layer 
is maintained at the end for the classification phase. While deep 
learning-based  feature extraction and classification are often very 
accurate, most of them usually require strong computational 
power for their perusal. In contrast to these traditional deep learning-based frameworks, in WaveDIF, the feature 
extraction phase is based on wavelet sub-band energies 
extracted through Discrete Wavelet Transform (DWT), which 
enables decomposition of the video frames into different 
frequency components while preserving spatial locality. This 
further allows us to simultaneously examine both high and 
low-frequency artifacts introduced during deepfake synthesis. 
Prior to wavelet decomposition, Discrete Fourier 
Transform (DFT) for each frame of the input video is performed, 
which filters out high frequency artifacts that get 
added to the videos during deepfake synthesis. 

Fig.~\ref{fig:face_forensics_intro} shows the result of
applying DFT for selected frames of videos from the
\texttt{FaceForensics++} dataset~\cite{rossler2019faceforensics++}. It 
also shows the difference heatmap (corresponding to original and 
different deepfake representations) to elucidate the relevance 
of of our approach of classifying deepfakes in the 
frequency domain. The classification phase in 
WaveDIF relies on the computed 
(sub-band - LL, LH, HL, and HH) energy values, based on
which a decision boundary (along with a threshold) is learned 
through regression analysis. Classification of new (unseen) 
video examples is through the application of these decision
boundary. Fig.~\ref{fig:band_plots_ff++} shows the DWT sub-bands’ three-dimensional visualization (for a selected video pair from \texttt{FaceForensics++} ,i.e., original and corresponding five deepfakes, where the $x$, and $y$ axes represents the spatial dimensions, and $z$ axis represents the wavelet coefficients’ magnitude. This enables pictorial visualization of the features used for classifying the videos, and the marked differences (with yellow dots) justifies the relevance of wavelet sub-bands' energies for the classification. 

A common practice with frequency domain deepfake detection models 
is to utilize high-frequency artifacts for the classification. 
\textbf{In contrast, in this research high-frequency
details are filtered out, since wavelet decomposition 
is a multi-resolution analysis which is very sensitive to 
noise and high-frequency distortions}. In particular, 
to get rid of spurious high-frequency noise, Gaussian 
low-pass filter was used. 
%This also aligns to Heisenberg’s Uncertainty Principle (regarding trade-off between  spatial localization and frequency resolution). 
Direct utilization of the DWT coefficients for 
classification leads to poor localization in the frequency 
domain due to the widespread noise across all sub-bands.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure*}[htbp]
    \centering
    \makebox[0.196\linewidth]{Input Video}
    \makebox[0.196\linewidth]{LL (\textit{Low-Low})}
    \makebox[0.196\linewidth]{LH (\textit{Low-High})}
    \makebox[0.196\linewidth]{HL (\textit{High-Low})}
    \makebox[0.196\linewidth]{HH (\textit{High-High})}
    
    \textbf{\\}
    \includegraphics[width=0.196\linewidth]{original_intro_.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/LL_original.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/LH_original.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/HL_original.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/HH_original.png}
   
    \includegraphics[width=0.196\linewidth]{deepfake_intro_.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/LL_diff.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/LH_diff.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/HL_diff.png}
    \includegraphics[width=0.196\linewidth]{sec/band_plots_ff++/HH_diff.png}


    \caption{\label{fig:band_plots_ff++} Motivation for using wavelet sub-bands (and their energies) as a distinguishable feature between real and deepfake video. The \textbf{first row} corresponds to energy sub-band visualization (in 3D) for a \textbf{real video}, while the \textbf{second row} corresponds to visualization (in 3D) for a \textbf{synthetically prepared video}. Note that in the visualizations of the second row, yellow dots represent coefficients with difference in magnitude.}
\end{figure*} 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

To evaluate the effectiveness of the proposed 
WaveDIF technique, two popular deepfake (video-only) 
datasets -- \texttt{FaceForensics++}~\cite{rossler2019faceforensics++}, 
and \texttt{CelebDF (v2)}~\cite{li2019celeb} have been considered. 
The evaluation results 
%(based on preset, standard classification metrics)  
shows competitive performance by WaveDIF compared to 
state-of-the-art deep learning based deepfake detection frameworks 
across all domain, \textbf{while incurring much lesser
computational cost}. \textbf{WaveDIF achieves 
$\approx 94.93\%$ in-dataset, and $\approx 88.83\%$ cross-dataset 
accuracies for the \texttt{FaceForensics++} dataset. Similarly, 
for the \texttt{CelebDF (v2)} dataset, the metrics are 
$\approx 92.03\%$, and  $\approx 87.01\%$ respectively.} 

To sum up, the novelties of the proposed WaveDIF 
methodology in contrast to existing frameworks are as follows:
\begin{enumerate}
\item Existing deepfake detection techniques mostly rely on 
features extracted from the spatial (standalone) or fusion 
features from spatial domain with those from other domains 
like audio, spectra, etc. 
\cite{naskar2024deepfake, usmani2025spatio, astrid2025audio}, but 
WaveDIF operates strictly on features extracted 
from the frequency domain.

\item Existing techniques mostly rely on deep learning-based 
feature extraction, making them computationally expensive 
(hidden layers like attention~\cite{khormali2021add,ganguly2022visual} 
and convolution \cite{omar2024ensemble, roy20223d} are 
computationally expensive). In contrast, WaveDIF 
uses Discrete Wavelet Transformation (DWT) to decompose 
video frames into LL, LH, HL, HH sub-bands, and then to 
their respective energies $\mathcal{E}_{\text{LL}}, \mathcal{E}_{\text{LH}}, \mathcal{E}_{\text{HL}}$, and $ \mathcal{E}_{\text{HH}}$, 
which makes the feature extraction stage lightweight.

\item Existing techniques mostly rely on fully-connected 
layers for classification~\cite{patel2023improved, deng2022deepfake}, 
which have hereby been replaced by linear and logistic regression 
to model an interpretable decision boundary with a threshold. 
This makes the classification both lightweight and interpretable.

\end{enumerate}


The rest of the manuscript is organized as follows – Section \ref{sec:related} presents related works on deepfake detection (across the two primary domains – spatial, and frequency). Section \ref{sec:proposal} explains in detail the proposed methodology - WaveDIF. Section \ref{sec:experiments} is directed towards evaluating the proposed methodology, experimenting with deepfake datasets, comparing the performance of WaveDIF with respect to the state-of-the-art detection frameworks. Finally, the paper concludes 
with comments about direction of future work in Section~\ref{sec:conclusion}.

% \begin{figure}[t]
%   \centering
%    \includegraphics[width=\linewidth]{sec/faceswap_intro.pdf}

%    \caption{\label{fig:faceswap_intro} An example of Deepfake, generated through \textsc{FaceSwap} \cite{nirkin2019fsgan}. Input to the generator are two media – target, and source (face). The \textsc{FaceSwap} algorithm first identifies the face mask from the source, and then concatenates (represented here as $\oplus$) the face mask on the target.}
% \end{figure}









% \begin{figure*}[!t]
%   \centering
%    \includegraphics[width=\linewidth]{sec/face_forensics_intro.pdf}

%    \caption{\label{fig:face_forensics_intro} Instances corresponding to a selected video from the \texttt{FaceForensics++} \cite{rossler2019faceforensics++} dataset (first row). Corresponding to each original video, five deepfakes – \textsc{Deepfakes} \cite{kaur2020deepfakes}, \textsc{Face2Face} \cite{thies2019face2face}, \textsc{FaceShifter} \cite{li2019faceshifter}, \textsc{FaceSwap} \cite{nirkin2019fsgan}, and \textsc{Neural Textures} \cite{thies2019deferred} are generated. The second row shows the face masks corresponding to each frame from the first row; these are our region of interest. The third row shows the Discrete Fourier transformation of the frames. Though difficult to visualize, the DFTs differ from each other considerably; SSIM (Original, \textsc{Deepfakes}) = 0.6028, SSIM (Original, \textsc{Face2Face}) = 0.6010, SSIM (Original, \textsc{FaceShifter}) = 0.5811, SSIM (Original, \textsc{FaceSwap}) = 0.5823, SSIM (Original, \textsc{Neural Textures}) = 0.5991. The fourth row shows the difference heatmap where bright red or yellow areas denotes regions of major modifications, in green or blue are regions of moderate to low modifications. }
% \end{figure*}
















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