Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis
This work addresses the problem of detecting forged images for forensic applications, but it is incremental as it applies existing deep learning models to a known dataset with a standard preprocessing technique.
The paper tackled forged image detection by applying transfer learning with state-of-the-art image classification models (e.g., VGG-19, ResNet-152-V2) on the CASIA ITDE v.2 dataset enhanced with Error Level Analysis, achieving results that demonstrate the effectiveness of these methods for this binary classification problem.
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.