DINO-VPT: Hierarchical Visual Prompt Tuning for Joint Physical-Digital Face Anti-Spoofing
Quick Answer
DINO-VPT introduces a lightweight, vision-only framework for face anti-spoofing, achieving superior accuracy over existing VLM-based methods on the UniAttackData benchmark without multimodal fusion.
Quick Take
This approach utilizes hierarchical visual prompt tuning to effectively address diverse spoofing threats, demonstrating that vision-only architectures can excel in unified FAS tasks.
Key Points
- DINO-VPT leverages hierarchical visual prompt tuning for face anti-spoofing.
- Achieves higher accuracy than state-of-the-art methods on UniAttackData.
- No multimodal fusion or external text encoders are required.
- Demonstrates effectiveness against both physical and digital spoofing attacks.
- Accepted for presentation at IJCB2026.
Paper Resources
Source Excerpt
With the increasing diversity of spoofing attacks, there is a growing demand for unified Face Anti-Spoofing (FAS) models capable of detecting both physical and digital threats. While existing (VLMs) demonstrate high generalization in this context, they heavily rely on complex multimodal fusion and external text encoders. In this paper, we propose DINO-VPT, a lightweight, vision-only framework leveraging hierarchical visual prompt tuning. By dynamically injecting prompts co
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