  Along with the widespread use of face recognition systems, their
vulnerability has become highlighted. While existing face anti-spoofing methods
can be generalized between attack types, generic solutions are still
challenging due to the diversity of spoof characteristics. Recently, the spoof
trace disentanglement framework has shown great potential for coping with both
seen and unseen spoof scenarios, but the performance is largely restricted by
the single-modal input. This paper focuses on this issue and presents a
multi-modal disentanglement model which targetedly learns polysemantic spoof
traces for more accurate and robust generic attack detection. In particular,
based on the adversarial learning mechanism, a two-stream disentangling network
is designed to estimate spoof patterns from the RGB and depth inputs,
respectively. In this case, it captures complementary spoofing clues inhering
in different attacks. Furthermore, a fusion module is exploited, which
recalibrates both representations at multiple stages to promote the
disentanglement in each individual modality. It then performs cross-modality
aggregation to deliver a more comprehensive spoof trace representation for
prediction. Extensive evaluations are conducted on multiple benchmarks,
demonstrating that learning polysemantic spoof traces favorably contributes to
anti-spoofing with more perceptible and interpretable results.
