Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network
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Abstract (arXiv)
Image Forgery is a problem of image forensics and its detection can be leveraged using Deep Learning. In this paper we present an approach for identification of authentic and tampered images done using image editing tools with Error Level Analysis and Convolutional Neural Network. The process is performed on CASIA ITDE v2 dataset and trained for 50 and 100 epochs respectively. The respective accuracies of the training and validation sets are represented using graphs.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 19 Apr 2022.
Builds on
8 of 26 references.
References · 8
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.