Department of Software Engineering, University of, Momina Masood, , Tahira Nazir, " FaceSwap based DeepFakes Detection Marriam Nawaz", The International Arab Journal of Information Technology (IAJIT) ,Volume 19, Number 06, pp. 79 - 84, November 2022, doi: 10.34028/iajit/19/6/6 .

Abstract: The progression of Machine Learning (ML) has introduced new trends in the area of image processing. Moreover, ML presents lightweight applications capable of running with minimum computational resources like Deepfakes, which generates widely manipulated multimedia data. Deepfakes introduce a serious danger to the confidentiality of humans and bring extensive religion, sect, and political anxiety. The FaceSwapp-based deepfakes are problematic to be identified by people due to their realism. Hence, the researchers are facing serious issues to detect visual manipulations. In the presented approach, we have proposed a novel technique for recognizing FaceSwap-based deepfakes. Initially, landmarks are computed from the input videos by employing Dlib-library. In the next step, the computed landmarks are used for training two classifiers namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The reported results demonstrate that SVM works well than ANN in classifying the manipulated samples due to its power to deal with over-fitted training data.
URL: https://iajit.org/paper/403

@ARTICLE{403,
author={Department of Software Engineering, University of, Momina Masood, , Tahira Nazir},
journal={The International Arab Journal of Information Technology (IAJIT)},
title={FaceSwap based DeepFakes Detection Marriam Nawaz},
volume={19},
number={06},
pages={79 - 84},
doi={10.34028/iajit/19/6/6 },
year={1970}

,abstract={The progression of Machine Learning (ML) has introduced new trends in the area of image processing. Moreover, ML presents lightweight applications capable of running with minimum computational resources like Deepfakes, which generates widely manipulated multimedia data. Deepfakes introduce a serious danger to the confidentiality of humans and bring extensive religion, sect, and political anxiety. The FaceSwapp-based deepfakes are problematic to be identified by people due to their realism. Hence, the researchers are facing serious issues to detect visual manipulations. In the presented approach, we have proposed a novel technique for recognizing FaceSwap-based deepfakes. Initially, landmarks are computed from the input videos by employing Dlib-library. In the next step, the computed landmarks are used for training two classifiers namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The reported results demonstrate that SVM works well than ANN in classifying the manipulated samples due to its power to deal with over-fitted training data.},
keywords={Deepfakes, faceswap, ANN, SVM},
ISSN={2413-9351},
month={Jan}}

TY - JOUR
TI - FaceSwap based DeepFakes Detection Marriam Nawaz
T2 -
SP - 79
EP - 84
AU - Department of Software Engineering
AU - University of
AU - Momina Masood
AU -
AU - Tahira Nazir
DO - 10.34028/iajit/19/6/6
JO - The International Arab Journal of Information Technology (IAJIT)
IS - 9
SN - 2413-9351
VO - 19
VL - 19
JA -
Y1 - Jan 1970
ER -
PY - 1970

AB - The progression of Machine Learning (ML) has introduced new trends in the area of image processing. Moreover, ML presents lightweight applications capable of running with minimum computational resources like Deepfakes, which generates widely manipulated multimedia data. Deepfakes introduce a serious danger to the confidentiality of humans and bring extensive religion, sect, and political anxiety. The FaceSwapp-based deepfakes are problematic to be identified by people due to their realism. Hence, the researchers are facing serious issues to detect visual manipulations. In the presented approach, we have proposed a novel technique for recognizing FaceSwap-based deepfakes. Initially, landmarks are computed from the input videos by employing Dlib-library. In the next step, the computed landmarks are used for training two classifiers namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The reported results demonstrate that SVM works well than ANN in classifying the manipulated samples due to its power to deal with over-fitted training data.