# Detecting and Localizing Copy-Move and Image-Splicing Forgery

arXiv: https://arxiv.org/abs/2202.04069 · HTML: https://mlsanity.com/paper/2202.04069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier](https://mlsanity.com/paper/1802.04881/) (2018)
- [D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization](https://mlsanity.com/paper/2012.01821/) (2020)
- [Camera-based Image Forgery Localization using Convolutional Neural Networks](https://mlsanity.com/paper/1808.09714/) (2018)

## Abstract (arXiv)

In the world of fake news and deepfakes, there have been an alarmingly large number of cases of images being tampered with and published in newspapers, used in court, and posted on social media for defamation purposes. Detecting these tampered images is an important task and one we try to tackle. In this paper, we focus on the methods to detect if an image has been tampered with using both Deep Learning and Image transformation methods and comparing the performances and robustness of each method. We then attempt to identify the tampered area of the image and predict the corresponding mask. Based on the results, suggestions and approaches are provided to achieve a more robust framework to detect and identify the forgeries.