SynMorph: Generating Synthetic Face Morphing Dataset With Mated Samples

2024-09-09

IEEE Access

AI Summary

Facing the bottleneck of facial morphing attack detection (MAD) development—namely, scarcity of authentic data and stringent privacy constraints—this work introduces the first large-scale, publicly available synthetic face morphing dataset, comprising 2,450 identities and over 100,000 high-fidelity samples, covering both single-sample and differential morphing attack scenarios. We propose a novel morphing synthesis framework grounded in generative modeling and deformation mapping, enabling, for the first time, systematic generation of mated-sample morphs. Furthermore, we unify image quality assessment (IQA) and vulnerability analysis to jointly optimize performance across both MAD tasks. Extensive multi-protocol benchmarking demonstrates that our dataset significantly outperforms existing state-of-the-art synthetic datasets, achieving detection accuracy comparable to that attained on real-world data—thereby validating its effectiveness and strong generalizability for training diverse MAD models.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection scenarios. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic dataset and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.

Problem

• Lack of large-scale public face morphing datasets due to privacy
• Need for synthetic dataset with diverse morphing algorithms
• Evaluating synthetic dataset's quality and attack detection performance

Innovation

• Generates synthetic face morphing dataset
• Includes 2450 identities and 100k morphs
• Supports single and differential MAD algorithms