# Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection

arXiv: https://arxiv.org/abs/2007.03621 · HTML: https://mlsanity.com/paper/2007.03621/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 30 of 46)

- [Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors](https://mlsanity.com/paper/2203.06691/) (2022)
- [MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders](https://mlsanity.com/paper/2302.01843/) (2023)
- [SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2208.07337/) (2022)
- [PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection](https://mlsanity.com/paper/2108.10291/) (2021)
- [Are GAN-based Morphs Threatening Face Recognition?](https://mlsanity.com/paper/2205.02496/) (2022)
- [ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation](https://mlsanity.com/paper/2108.09130/) (2021)
- [Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection](https://mlsanity.com/paper/2208.05787/) (2022)
- [Attention Aware Wavelet-based Detection of Morphed Face Images](https://mlsanity.com/paper/2106.15686/) (2021)
- [Leveraging Diffusion For Strong and High Quality Face Morphing Attacks](https://mlsanity.com/paper/2301.04218/) (2023)
- [MADation: Face Morphing Attack Detection with Foundation Models](https://mlsanity.com/paper/2501.03800/) (2025)
- [Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer](https://mlsanity.com/paper/2501.09817/) (2025)
- [Differential Morph Face Detection using Discriminative Wavelet Sub-bands](https://mlsanity.com/paper/2106.13178/) (2021)
- [OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement](https://mlsanity.com/paper/2208.07841/) (2022)
- [Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs](https://mlsanity.com/paper/2404.06025/) (2024)
- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality](https://mlsanity.com/paper/2208.05864/) (2022)
- [MorDeephy: Face Morphing Detection Via Fused Classification](https://mlsanity.com/paper/2208.03110/) (2022)
- [SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning](https://mlsanity.com/paper/2504.05504/) (2025)
- [Focused LRP: Explainable AI for Face Morphing Attack Detection](https://mlsanity.com/paper/2103.14697/) (2021)
- [Deep Composite Face Image Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2211.11039/) (2022)
- [MorCode: Face Morphing Attack Generation using Generative Codebooks](https://mlsanity.com/paper/2410.07625/) (2024)
- [Algorithmic Fairness in Face Morphing Attack Detection](https://mlsanity.com/paper/2111.12115/) (2021)
- [3D Face Morphing Attacks: Generation, Vulnerability and Detection](https://mlsanity.com/paper/2201.03454/) (2022)
- [Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection](https://mlsanity.com/paper/2305.18216/) (2023)
- [Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes](https://mlsanity.com/paper/2311.17655/) (2023)
- [Worst-Case Morphs: a Theoretical and a Practical Approach](https://mlsanity.com/paper/2111.15416/) (2021)
- [TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks](https://mlsanity.com/paper/2401.11598/) (2024)
- [Approximating Optimal Morphing Attacks using Template Inversion](https://mlsanity.com/paper/2402.00695/) (2024)
- [The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios](https://mlsanity.com/paper/2404.06559/) (2024)
- [Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection](https://mlsanity.com/paper/2410.16802/) (2024)

## Abstract (arXiv)

The primary objective of face morphing is to combine face images of different data subjects (e.g. a malicious actor and an accomplice) to generate a face image that can be equally verified for both contributing data subjects. In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN. In contrast to earlier works, we generate realistic morphs of both high-quality and high resolution of 1024×1024 pixels. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work. textit(i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs? Seeking an answer, we benchmark the vulnerability of a Commercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace). This work also benchmarks the detection approaches for both GAN generated morphs against the landmark based morphs using established Morphing Attack Detection (MAD) schemes.