A Commercial Web Application for Image Deepfake Detection Using CNN Model

Abstract

This study introduces an innovative methodology for the early prediction
of deepfake. The rise of deep learning has facilitated the creation of fraudulent
digital content, commonly known as deepfakes, which closely mimic authentic
digital content, posing a significant threat to truth. Deepfakes, driven by
artificial intelligence, possess the ability to elude conventional detection
techniques through adaptive learning, raising concerns about their elusive
nature. This research is dedicated to addressing the challenge of detecting
deepfake images using a custom Convolutional Neural Network (CNN) based
deep learning model. The model is specifically designed to identify deepfake
content by analyzing subtle artifacts and inconsistencies inherent in
manipulated images. Additionally, the research explores the integration of
Error Level Analysis (ELA), a forensic technique highlighting compression
artifacts, to enhance the model's accuracy in discerning anomalies indicative of
deepfake manipulation.

Keywords: Deep Learning, Deepfake, Deepfake Detection, Convolutional Neural Networks (CNNs), Error Level Analysis (ELA), Media forensics, deepfake manipulation.