Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 28 Nov 2022.
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22 of 41 references.
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