Decoherence-Aware Quantum State Evolution With Identity-Liveness Consistency for Robust Face Anti-Spoofing
Face anti-spoofing is essential for protecting biometric systems from presentation attacks such as print, replay, cut-photo, and deepfake manipulations. This work proposes a Decoherence-Aware Quantum State and Liveness Detection Framework for robust live-spoof discrimination. Facial video frames are first preprocessed and then passed through multi-scale convolutional networks to capture fine-grained spoof traces, such as moiré patterns, reflection noise, and display artifacts. The extracted features are then transformed into latent temporal states using superposition modeling and reversible temporal evolution. A stable evolution-adaptive transition mechanism is introduced to detect temporal disturbances induced by spoofing. Further, identity-liveness dependency stability modeling improves robustness against identity-preserving attacks. Quantum uncertainty-guided anomaly scoring is used for final spoof discrimination, followed by a lightweight MLP classifier. Experimental results on the CelebA-Spoof, OULU-NPU, and CASIA-FASD benchmark datasets demonstrate that the proposed framework achieves consistent performance across these evaluated datasets. The reported robustness and generalization are supported within the scope of these benchmark evaluations. On CelebA-Spoof, the model achieved APCER of 1.78%, BPCER of 2.29%, and ACER of 2.04%. On OULU-NPU, APCER, BPCER, and ACER were 1.38%, 1.42%, and 1.40%, respectively. On CASIA-FASD, the framework achieved 96.99% multiclass spoof classification accuracy, confirming better robustness and generalization.