# VIPPrint: A Large Scale Dataset of Printed and Scanned Images for Synthetic Face Images Detection and Source Linking

arXiv: https://arxiv.org/abs/2102.06792 · HTML: https://mlsanity.com/paper/2102.06792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Detecting GAN generated Fake Images using Co-occurrence Matrices](https://mlsanity.com/paper/1903.06836/) (2019)
- [Video Face Manipulation Detection Through Ensemble of CNNs](https://mlsanity.com/paper/2004.07676/) (2020)
- [CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis](https://mlsanity.com/paper/2007.12909/) (2020)
- [Learning to Detect Fake Face Images in the Wild](https://mlsanity.com/paper/1809.08754/) (2018)
- [A Survey of Machine Learning Techniques in Adversarial Image Forensics](https://mlsanity.com/paper/2010.09680/) (2020)
- [Source Printer Classification using Printer Specific Local Texture Descriptor](https://mlsanity.com/paper/1806.06650/) (2018)
- [Printing and Scanning Attack for Image Counter Forensics](https://mlsanity.com/paper/2005.02160/) (2020)
- [Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents](https://mlsanity.com/paper/1706.06651/) (2017)
- [Single Classifier-based Passive System for Source Printer Classification using Local Texture Features](https://mlsanity.com/paper/1706.07422/) (2017)

## Abstract (arXiv)

The possibility of carrying out a meaningful forensics analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography pictures, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.