Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis

TL;DR

This paper explores the use of transfer learning with state-of-the-art CNN models to detect forged images through Error Level Analysis, demonstrating effective binary classification on a specialized dataset.

Contribution

It applies transfer learning with multiple advanced CNN architectures to improve forged image detection using Error Level Analysis, a novel combination in this context.

Findings

VGG-19 achieved high accuracy in detection

Inception-V3 showed robust performance

EfficientNet-V2L outperformed other models

Abstract

The advancement in the area of computer vision has been brought using deep learning mechanisms. Image Forensics is one of the major areas of computer vision application. Forgery of images is sub-category of image forensics and can be detected using Error Level Analysis. Using such images as an input, this can turn out to be a binary classification problem which can be leveraged using variations of convolutional neural networks. In this paper we perform transfer learning with state-of-the-art image classification models over error level analysis induced CASIA ITDE v.2 dataset. The algorithms used are VGG-19, Inception-V3, ResNet-152-V2, XceptionNet and EfficientNet-V2L with their respective methodologies and results.