# Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network

arXiv: https://arxiv.org/abs/2204.09075 · HTML: https://mlsanity.com/paper/2204.09075/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Pooling Methods in Deep Neural Networks, a Review](https://mlsanity.com/paper/2009.07485/) (2020)
- [Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis](https://mlsanity.com/paper/2201.00418/) (2022)
- [Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication](https://mlsanity.com/paper/2201.03029/) (2022)
- [Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method](https://mlsanity.com/paper/2202.07779/) (2022)
- [Combining Varied Learners for Binary Classification using Stacked Generalization](https://mlsanity.com/paper/2202.08910/) (2022)

## Built on by (most cited first, 2 of 2)

- [Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis](https://mlsanity.com/paper/2211.15196/) (2022)
- [Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification](https://mlsanity.com/paper/2303.14401/) (2023)

## 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.