# The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios

arXiv: https://arxiv.org/abs/2404.06559 · HTML: https://mlsanity.com/paper/2404.06559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Diffusion Autoencoders: Toward a Meaningful and Decodable Representation](https://mlsanity.com/paper/2111.15640/) (2021)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Partial FC: Training 10 Million Identities on a Single Machine](https://mlsanity.com/paper/2010.05222/) (2020)
- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection](https://mlsanity.com/paper/2007.03621/) (2020)
- [Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking](https://mlsanity.com/paper/2006.06458/) (2020)
- [Are GAN-based Morphs Threatening Face Recognition?](https://mlsanity.com/paper/2205.02496/) (2022)
- [How to Scale Your EMA](https://mlsanity.com/paper/2307.13813/) (2023)
- [Face morphing detection in the presence of printing/scanning and heterogeneous image sources](https://mlsanity.com/paper/1901.08811/) (2019)
- [Morphing Attack Potential](https://mlsanity.com/paper/2204.13374/) (2022)
- [Leveraging Diffusion For Strong and High Quality Face Morphing Attacks](https://mlsanity.com/paper/2301.04218/) (2023)
- [On the detection of morphing attacks generated by GANs](https://mlsanity.com/paper/2209.00404/) (2022)
- [Fast-DiM: Towards Fast Diffusion Morphs](https://mlsanity.com/paper/2310.09484/) (2023)

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

- [Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications](https://mlsanity.com/paper/2408.09558/) (2024)
- [On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection](https://mlsanity.com/paper/2604.20585/) (2026)

## Abstract (arXiv)

Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vulnerabilities in FRs. These systems are particularly susceptible to attacks when the morphs are subjected to print-scanning to mask the artifacts generated during the morphing process. We investigate the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios. Our experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. Furthermore, when a Single-image Morphing Attack Detection (S-MAD) algorithm is not trained to detect print-scanned morphs the Morphing Attack Classification Error Rate (MACER) can increase by up to 96.12%, indicating significant vulnerability.