Face verification systems are prone to spoofing attacks on photos, videos, and 3D masks. Face spoofing detection, i.e., face anti-spoofing, face liveness detection, or face presentation attack detection, is an important task for securing face verification systems in practice and presents many challenges. In this paper, a state-of-the-art face spoofing detection method based on a depth-based Fully Convolutional Network (FCN) is revisited. Different supervision schemes, including global and local label supervisions, are comprehensively investigated. A generic theoretical analysis and associated simulation are provided to demonstrate that local label supervision is more suitable than global label supervision for local tasks with insufficient training samples, such as the face spoofing detection task. Based on the analysis, the Spatial Aggregation of Pixel-level Local Classifiers (SAPLC), which is composed of an FCN part and an aggregation part, is proposed. The FCN part predicts the pixel-level ternary labels, which include the genuine foreground, the spoofed foreground, and the undetermined background. Then, these labels are aggregated together to yield an accurate image-level decision. Furthermore, to quantitatively evaluate the proposed SAPLC, experiments are carried out on the CASIA-FASD, Replay-Attack, OULU-NPU, and SiW datasets. The experiments show that the proposed SAPLC outperforms the representative deep networks, including two globally supervised CNNs, one depth-based FCN, two FCNs with binary labels, and two FCNs with ternary labels, and achieves competitive performances close to some state-of-the-art method performances under various common protocols. Overall, the results empirically verify the advantage of the proposed pixel-level local label supervision scheme.

In this paper, a face spoofing detection method called the Fully Convolutional Network with Domain Adaptation and Lossless Size Adaptation (FCN-DA-LSA) is proposed. As its name suggests, the FCN-DA-LSA includes a lossless size adaptation preprocessor followed by an FCN based pixel-level classifier embedded with a domain adaptation layer. The FCN local classifier makes full use of the basic properties of face spoof distortion namely ubiquitous and repetitive. The domain adaptation (DA) layer improves generalization across different domains. The lossless size adaptation (LSA) preserves the highfrequent spoof clues caused by the face recapture process. The ablation study shows that both DA and the LSA are necessary for high-accuracy face spoofing detection. The FCN-LSA obtains competitive performance among the state-of-the-art methods. With the help of small-sample external data in the target domain (2/50, 2/50, and 1/20 subjects for CASIA-FASD, Replay-Attack, and OULU-NPU respectively), the FCN-DA-LSA further improves the performance and outperforms the existing methods.

Aiming at the problem that the structural complexity of the backbone architecture of denoising diffusion probabilistic model (DDPM) leads to the inefficiency of the training of the model, a lightweight image generation method (MobileDiT) based on MobileViT is proposed, which takes MobileViT block as the backbone architecture of DDPM, and improves the computational efficiency while keeping the image quality by combining lightweight convolution with Transformer. The conditional mechanism for introducing conditional information into the model is also improved by replacing the traditional layer normalization in the Transformer block with adaptive layer normalization and initialising each block as a constant function, allowing the model to process conditional information more efficiently. The experiment results show that the proposed model reduces the FID-50K to 2.15 and improves the IS value compared with models such as Style Generative Adversarial Network (StyleGAN) and Ablative Diffusion Model (ADM). This shows that the proposed model can not only improve the computational efficiency, but also enhance the quality of the generated images.

As one of the most crucial parts of face detection, the accuracy and the generalization of face anti-spoofing are particularly important. Therefore, it is necessary to propose a multi-branch network to improve the accuracy and generalization of the detection of unknown spoofing attacks. These branches consist of several frequency map encoders and one depth map encoder. These encoders are trained together. It leverages multiple frequency features and generates depth map features. High-frequency edge texture is beneficial for capturing moiré patterns, while low-frequency features are sensitive to color distortion. Depth maps are more discriminative than RGB images at the visual level and serve as useful auxiliary information. Supervised Multi-view Contrastive Learning enhances multi-view feature learning. Moreover, a two-stage feature fusion method effectively integrates multi-branch features. Experiments on four public datasets, namely CASIA-FASD, Replay–Attack, MSU-MFSD, and OULU-NPU, demonstrate model effectiveness. The average Half Total Error Rate (HTER) of our model is 4% (25% to 21%) lower than the Adversarial Domain Adaptation method in inter-set evaluations.