Multi-Tiered Fake Image Analysis: A Unified Deep and Machine Learning Approach

Paper ID: EIJTEM_2025_12_4_59-65

Author's Name: Murshida B, Dr. Shameem K

Volume: 12

Issue: 4

Year: 2025

Page No: 59-65

Abstract:

Deepfake technology generates realistic but fake images, videos, or audio, often used for deception and misinformation. These deepfakes, created with advanced deep learning, pose significant dangers by manipulating public opinion, damaging reputations, violating privacy, and threatening cybersecurity. Their ability to erode trust in media highlights the substantial harm they can cause. Detecting deepfake images is crucial for maintaining digital media integrity. This research develops a robust detection model by combining Error Level Analysis (ELA) with Convolutional Neural Networks (CNN) and Decision Tree-based Metadata Analysis, providing a multi-tiered approach to expose deepfakes and other manipulated images.

Keywords: classification, error level analysis, Convolutional neural network, decision tree, glcm

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