\section{Conclusion}\label{sec:conclusion}
This work introduced WaveDIF, which uses wavelet sub-band energies for the detection of deepfake videos. Unlike traditional methods that use deep learning for feature extraction, WaveDIF has a lightweight but efficient pipeline that uses DFT for high-frequency noise dropout, followed by DWT to extract sub-band energies as distinguishing features. WaveDIF outperforms existing frequency domain deepfake detection techniques, though like any other frequency-based approach, WaveDIF falls short of its’ accuracy compared to spatial domain methods. But, in contrast to the exponentially high count of model parameters needed to be trained for multi-modal methods, WaveDIF have significantly lesser number of parameters, which also gets reflected in its’ lightweight model complexity. Future works can be conducted on the integration of temporal dynamics and adaptive thresholding to achieve generalizability.

% \section*{Acknowledgement}
%   This work is partially funded by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Govt. of India, Grant No. EEQ/2023/000074, dated: 20/12/2023.
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