International Journal For Multidisciplinary Research

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Deep Fake Video Detection

Author(s) K. B. Nikumbh, Vaibhav Adhe, Sahil Karamkar, Shivam Ghaware, Shruti Khaire
Country India
Abstract The growing computation power has made the deep learning algorithms so powerful that creating a indistinguishable human synthesized video popularly called as
deep fakes have became very simple. Scenarios where these realistic face swapped
deep fakes are used to create political distress, fake terrorism events, revenge porn,
blackmail peoples are easily envisioned. In this work, we describe a new deep
learning-based method that can effectively distinguish AI-generated fake videos
from real videos.Our method is capable of automatically detecting the replacement
and reenactment deep fakes. We are trying to use Artificial Intelligence(AI) to fight
Artificial Intelligence(AI). Our system uses a Res-Next Convolution neural network
to extract the frame-level features and these features and further used to train the
Long Short Term Memory(LSTM) based Recurrent Neural Network(RNN) to classify whether the video is subject to any kind of manipulation or not, i.e whether the
video is deep fake or real video. To emulate the real time scenarios and make the
model perform better on real time data, we evaluate our method on large amount of
balanced and mixed data-set prepared by mixing the various available data-set like
Face-Forensic++[1], Deepfake detection challenge[2], and Celeb-DF[3]. We also
show how our system can achieve competitive result using very simple and robust
approach
Keywords Res-Next Convolution neural network. Recurrent Neural Network (RNN). Long Short Term Memory(LSTM). Computer vision
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-23
Cite This Deep Fake Video Detection - K. B. Nikumbh, Vaibhav Adhe, Sahil Karamkar, Shivam Ghaware, Shruti Khaire - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35345
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.35345
Short DOI https://doi.org/g82wpw
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