Abstract

The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized. But the training dataset for such a task is still nonstandard. Previous researchers have used unconventional and deviating datasets to train neural networks for detecting image forgeries and localizing pixel maps of manipulated regions. For a fair comparison, the training set, test set, and evaluation metrics should be persistent. Hence, comparing the existing methods may not seem fair as the results depend heavily on the training datasets as well as the model architecture. Moreover, none of the previous works release the synthetic training dataset used for the IMDL task. We propose a standardized benchmark training dataset for image splicing, copy-move forgery, removal forgery, and image enhancement forgery. Furthermore, we identify the problems with the existing IMDL datasets and propose the required modifications. We also train the state-of-the-art IMDL methods on our proposed TrainFors1 dataset for a fair evaluation and report the actual performance of these methods under similar conditions.

Metadata

publication

Proceedings of the IEEE/CVF International Conference on Computer Vision, 403-414, 2023

year

2023

publication date

2023

authors

Soumyaroop Nandi, Prem Natarajan, Wael Abd-Almageed

link

https://openaccess.thecvf.com/content/ICCV2023W/DFAD/html/Nandi_TrainFors_A_Large_Benchmark_Training_Dataset_for_Image_Manipulation_Detection_ICCVW_2023_paper.html

resource_link

https://openaccess.thecvf.com/content/ICCV2023W/DFAD/papers/Nandi_TrainFors_A_Large_Benchmark_Training_Dataset_for_Image_Manipulation_Detection_ICCVW_2023_paper.pdf

conference

Proceedings of the IEEE/CVF International Conference on Computer Vision

pages

403-414