MIDV-DM: A Document-Oriented Dataset for Image Manipulation Detection and Localization

As the scope of application of document recognition systems in business processes increases, so does the number of attacks on these systems. One form of such attacks could involve software for manipulating a digital image of a document. The development of methods for image manipulation detection and localization is complicated with the fact that available datasets neither contain images of documents nor lack diversity in capture conditions and document types. Furthermore, these datasets do not cover the range of possible kinds of manipulations that occur under natural conditions. In this paper, we introduce MIDV-DM – a publicly available benchmark designed for the development and testing of methods aimed at detecting and localizing manipulations in identity document images. It contains images subjected to eight types of manipulations, which we have conceptually categorized based on our analysis of over 2000 real-world fraud attempts. In total, MIDV-DM contains 1000 original document images from the public MIDV-2020 dataset and 8000 automatically created manipulated images based on them, along with the ground truth masks and annotations. The paper also describes the process of obtaining baseline quality based on the IML-ViT model. The authors believe that MIDV-DM will open new opportunities for researchers to advance technologies for document image authenticity analysis.