FaceSpoofLDM: Language-Guided Synthesis of Face Presentation Attacks Based on Latent Diffusion

Presentation Attacks (PAs) pose a serious threat to face recognition (FR) systems. These attacks cover a broad range of scenarios, including images replayed on various devices, printed photographs, or more sophisticated approaches such as 3D masks used to impersonate another identity. Recent advances in deep neural networks have led to an increasing number of face presentation attack detection (PAD) methods, replacing traditional approaches with great success. However, these methods are highly data-intensive and require large amounts of training data for reliable decision-making. Although several face PAD datasets have been introduced, they often come with restricted usage, limited subject and attack diversity and privacy or legal constraints. In this work, we introduce FaceSpoofLDM, a latent diffusion model (LDM) for language-guided image synthesis to generate synthetic face PAs and non-attacks across various demographic groups. Our approach reduces the need for manually crafting physical presentation attack instruments (PAI) while increasing scalability and attack diversity. Extensive experiments demonstrate the effectiveness of our model and show that incorporating synthetic PAIs, on average, enhances security against PAs.