Face recognition has achieved tremendous success in both its theory and technology. However, with increasingly realistic attacks, such as print photos, replay videos, and 3D masks, as well as new attack methods like AI-generated faces or videos, face recognition systems are confronted with significant challenges and risks. Distinguishing between real and fake faces, i.e., face anti-spoofing (FAS), is crucial to the security of face recognition systems. With the advent of large-scale academic datasets in recent years, FAS based on deep learning has achieved a remarkable level of performance and now dominates the field. This paper systematically reviews the latest advancements in FAS based on deep learning. First, it provides an overview of the background, basic concepts, and types of FAS attacks. Then, it categorizes existing FAS methods from the perspectives of RGB (red, green and blue) modality and other modalities, discussing the main concepts, the types of attacks that can be detected, their advantages and disadvantages, and so on. Next, it introduces popular datasets used in FAS research and highlights their characteristics. Finally, it summarizes the current research challenges and future directions for FAS, such as its limited generalization for unknown attacks, the insufficient multi-modal research, the spatiotemporal efficiency of algorithms, and unified detection for presentation attacks and deepfakes. We aim to provide a comprehensive reference in this field and to inspire progress within the FAS community, guiding researchers toward promising directions for future work.

Face recognition has been widely applied in identity authentication systems, but its vulnerability to various presentation attacks poses significant security risks. As a result, face anti-spoofing (FAS) has become one of the key technologies for ensuring the reliability of such systems. Most existing domain- generalized FAS (DGFAS) approaches rely on domain distribution alignment to learn cross-domain invariant representations. However, many of these methods independently model each sample while overlooking semantic consistency among cross-domain samples and different regions within each sample, making them vulnerable to semantic drift. Moreover, some existing methods introduce additional pixel-level supervision signals, such as pseudo-depth maps and binary masks, which increase annotation costs and limit their generalizability. To address these challenges, we propose DSCM-FAS, a novel framework that combines inter-sample and intra-sample semantic consistency to enhance robustness and cross-domain generalization, without requiring auxiliary supervision. Specifically, we design a Dual Semantic Consistency Module (DSCM). Across samples, contrastive learning is leveraged to learn discriminative representations, followed by the adaptive construction of a high-confidence similarity adjacency graph via statistical thresholding. The resulting graph is then fed into a graph convolutional network (GCN) to strengthen cross-domain semantic consistency, thereby improving the model's robustness to domain shifts. Within each sample, the intermediate embedding features are first partitioned into multiple patches, and Laplacian Regularization is introduced to constrain the semantic relationships among different patches. This effectively suppresses local noise interference and promotes the learning of more robust and semantically consistent feature representations. Extensive experiments on four public FAS datasets demonstrate that DSCM-FAS consistently outperforms state-of-the-art methods under various protocols. These results validate the effectiveness of our DSCM-FAS in improving the cross-domain generalization of FAS models.

Small objects in underwater and traffic-sign images often have weak boundaries and low contrast. Downsampling and multi-scale fusion can further weaken the features needed to detect them. We propose ContextFlow-YOLO, a YOLOv8n-based detector designed to preserve context during feature extraction and reuse it across scales. Its context-preserving backbone combines local features with surrounding and global information during downsampling. The global-guided scale-sequence neck distributes this context to features at different levels. A nonlinear context-attention fusion unit then refines the responses used for prediction. ContextFlow-YOLO obtains mAP@0.5 scores of 0.855, 0.838, and 0.826 on URPC2019, URPC2020, and TT100K, respectively. On TT100K, the model uses 6.11 M parameters and runs at 134 FPS. Heatmap and receptive-field visualizations show more concentrated target responses and broader feature coverage than YOLOv8n.