Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
Face Anti-Spoofing (FAS) is essential for ensuring the security and
reliability of facial recognition systems. Most existing FAS methods are
formulated as binary classification tasks, providing confidence scores without
interpretation. They exhibit limited generalization in out-of-domain scenarios,
such as new environments or unseen spoofing types. In this work, we introduce a
multimodal large language model (MLLM) framework for FAS, termed Interpretable
Face Anti-Spoofing (I-FAS), which transforms the FAS task into an interpretable
visual question answering (VQA) paradigm. Specifically, we propose a
Spoof-aware Captioning and Filtering (SCF) strategy to generate high-quality
captions for FAS images, enriching the model's supervision with natural
language interpretations. To mitigate the impact of noisy captions during
training, we develop a Lopsided Language Model (L-LM) loss function that
separates loss calculations for judgment and interpretation, prioritizing the
optimization of the former. Furthermore, to enhance the model's perception of
global visual features, we design a Globally Aware Connector (GAC) to align
multi-level visual representations with the language model. Extensive
experiments on standard and newly devised One to Eleven cross-domain
benchmarks, comprising 12 public datasets, demonstrate that our method
significantly outperforms state-of-the-art methods.
