IDENTIFYING DEEPFAKE IMAGES WITH ARTIFICIAL INTELLIGENCE - ENHANCED CONVOLUTIONAL NEURAL NETWORK

Abstract

The rapid advancement of deepfake technology poses significant challenges to the authenticity and integrity of digital media, leading to widespread concerns in various sectors, including politics, media, and personal relationships. This study aims to develop an AI-enhanced Convolutional Neural Network (CNN) model for detecting deepfake images, addressing the limitations of traditional detection methods that struggle against sophisticated manipulation techniques. By leveraging state-of-the-art deep learning architectures, specifically the XceptionNet model, this research explores the efficacy of advanced feature extraction techniques and data augmentation strategies to improve detection accuracy. The proposed system utilizes the FaceForensics++ dataset, which includes both authentic and manipulated images, to train and evaluate the model. Experimental results demonstrate that the AI-enhanced CNN model significantly outperforms traditional approaches, achieving a binary classification accuracy of 86.91% and a type classification accuracy of 70.50%. These findings indicate that the model effectively identifies subtle artifacts and inconsistencies that are characteristics of deepfake manipulations. This research not only contributes to the field of digital forensics but also emphasizes the need for ongoing advancements in detection methodologies to combat the evolving landscape of deepfake technology. Future work will focus on expanding the dataset, enhancing real-time detection capabilities, and integrating interdisciplinary approaches to address the broader societal implications of deepfakes. Ultimately, this study aims to empower individuals and organizations with reliable tools to discern authentic media from manipulated content, fostering a safer and more trustworthy digital environment.