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.
Kavita Wagh1, Mayank Hindka2, Telagamalla Gopi3, Syed Arfath Ahmed4
National Institute of Electronics and Information Technology, India1, Texas A&M University, United States of America2, Annamacharya Institute of Technology and Sciences, India3, Maulana Azad National Urdu University, India4
Support Vector Machine, Artificial Neural Networks, k-Nearest Neighbors, Decision Trees, Deep Fake Detection
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 3 , Pages: 3216 - 3221 )
Date of Publication :
February 2024
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335
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