A Machine Learning based Approach for DeepFake Detection

A Machine Learning based Approach for DeepFake Detection

1 Vidya H G, 2 Sindhu S L
1, Student ,Dept.of MCA, BIET, DVG

2, Assistant professor Dept.MCA, BIET, DVG

Abstract

Deepfake technology has raised significant concerns regarding the authenticity and reliability of digital media. Deepfakes, generated using advanced machine learning techniques such as Generative Adversarial Networks (GANs), can create hyper- realistic images and videos that are nearly indistinguishable from genuine content. This poses a substantial threat to various sectors, including security, media, and personal privacy. In response to this challenge, our research focuses on developing robust machine learning-based methods for the detection of deepfake images.We propose a comprehensive approach that leverages convolutional neural networks (CNNs) to analyze and differentiate between real and manipulated images. Our model is trained on a diverse dataset, encompassing various deepfake generation techniques to ensure its adaptability and accuracy. Preliminary results indicate that our method achieves high detection rates with notable precision and recall, outperforming existing baseline models.Furthermore, we explore the integration of explainable AI techniques to enhance the transparency and interpretability of our detection system. This research contributes to the ongoing efforts to safeguard digital integrity and provides a foundation for future advancements in automated deepfake detection technologies.

Keywords:Deepfake Detection ,Machine Learning Convolutional Neural Networks (CNNs) Generative Adversarial Networks (GANs) , Image Manipulation Detection , Fake Image Identification.