# Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis Source: [Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:54:03.791Z Authors: Raunak Joshi, Abhishek Gupta, Nandan Kanvinde, Pandharinath Ghonge Published: 2022-11-28T10:10:42.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()