ICTACT Journal on Image and Video Processing

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Ensemble Machine Learning Method for Detecting Deep Fakes in Social Platform


Affiliations
1 Department of Electronics and Telecommunication, National Institute of Electronics and Information Technology, India
2 Department of Computer Information Systems, Texas A&M University, United States
3 Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, India
4 Department of Computer Science and Engineering, Maulana Azad National Urdu University, India
     

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With the rise of deep fake technology, the detection of manipulated media has become crucial in maintaining the integrity of social platforms. In this study, we propose an ensemble machine learning approach combining Support Vector Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Decision Trees (DT) for deep fake detection. Our contribution lies in the development of a robust ensemble method that leverages the strengths of multiple algorithms to enhance detection accuracy and resilience against evolving deep fake techniques. Through experimentation on a diverse dataset, our ensemble model demonstrated superior performance compared to individual models, achieving high accuracy and robustness in detecting deep fakes on social platforms. Keywords: Deep fakes, Ensemble learning, Machine learning, Social platforms, Detection.

Keywords

Support Vector Machine, Artificial Neural Networks, k-Nearest Neighbors, Decision Trees, Deep Fake Detection.
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  • Ensemble Machine Learning Method for Detecting Deep Fakes in Social Platform

Abstract Views: 32  |  PDF Views: 0

Authors

Kavita Wagh
Department of Electronics and Telecommunication, National Institute of Electronics and Information Technology, India
Mayank Hindka
Department of Computer Information Systems, Texas A&M University, United States
Telagamalla Gopi
Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, India
Syed Arfath Ahmed
Department of Computer Science and Engineering, Maulana Azad National Urdu University, India

Abstract


With the rise of deep fake technology, the detection of manipulated media has become crucial in maintaining the integrity of social platforms. In this study, we propose an ensemble machine learning approach combining Support Vector Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Decision Trees (DT) for deep fake detection. Our contribution lies in the development of a robust ensemble method that leverages the strengths of multiple algorithms to enhance detection accuracy and resilience against evolving deep fake techniques. Through experimentation on a diverse dataset, our ensemble model demonstrated superior performance compared to individual models, achieving high accuracy and robustness in detecting deep fakes on social platforms. Keywords: Deep fakes, Ensemble learning, Machine learning, Social platforms, Detection.

Keywords


Support Vector Machine, Artificial Neural Networks, k-Nearest Neighbors, Decision Trees, Deep Fake Detection.

References