Deepfake Image Detection Using Machine Learning and Deep Learning

Abstract

This paper explores the effectiveness of machine learning and deep learning approaches in detecting deepfake images. With the increasing sophistication and prevalence of deepfake technology, it is becoming increasingly important to develop reliable methods for detecting manipulated images. The paper outlines the steps involved in creating a deepfake image detection model, including data preprocessing, dataset splitting, hyperparameter tuning, and the use of deep learning and machine learning techniques. Comprehensive review of the literature on deepfake detection, highlighting the challenges and limitations of existing approaches. They then propose a novel deep learning-based approach that leverages the power of convolutional neural networks (CNNs) to detect deepfake images. The proposed approach involves training a CNN on a large dataset of real and manipulated images, and using transfer learning to fine-tune the model on a smaller dataset of deepfake images. Here we try to evaluate the performance of their approach on a benchmark dataset of deepfake images, and compare it to several state-of-the-art deepfake detection methods. The results show that the proposed approach outperforms existing methods in terms of accuracy, precision, and recall. This paper provides valuable insights into the use of machine learning and deep learning approaches for deepfake image detection, and presents a promising new approach that could help to mitigate the risks of digital disinformation.