Efficient Net B5: A Robust Approach to Detect Morphed Images
DOI:
https://doi.org/10.15379/ijmst.v10i4.2379Keywords:
Convolutional Neural Networks, Event Detection, Morphological transformation, Efficient Net B5Abstract
Data traffic is important in the transmission and exchange of numerous types of data in the digital environment of the global web, including files and photographs. However, this data is susceptible to unauthorized alterations, particularly in the form of morphing, necessitating the development of effective detection mechanisms. The proposed system serves the purpose of identifying morphed images and informing users about their authenticityThe globe Wide Web's digital surroundings witnesses a constant flow of data in the form of files and photos. Unfortunately, data is not immune to tampering, and as a result, it becomes imperative to detect instances of such alterations. The proposed method aims to identify and flag modified photographs, providing users with insights into the authenticity of images they encounterThe academic industry has showed a renewed interest in addressing the subject of morph attack detection in recent years. Various studies and methodologies have been explored to accurately detect instances of morphing attacks and enhance the security of digital content.