Face anti-spoofing (FAS) plays a pivotal role in ensuring the security and reliability of face recognition systems. With advancements in vision-language pretrained (VLP) models, recent two-class FAS techniques have leveraged the advantages of using VLP guidance, while this potential remains unexplored in one-class FAS methods. The one-class FAS focuses on learning intrinsic liveness features solely from live training images to differentiate between live and spoof faces. However, the lack of spoof training data can lead one-class FAS models to inadvertently incorporate domain information irrelevant to the live/spoof distinction (\eg, facial content), causing performance degradation when tested with a new application domain. To address this issue, we propose a novel framework called Spoof-aware one-class face anti-spoofing with Language Image Pretraining (SLIP). Given that live faces should ideally not be obscured by any spoof-attack-related objects (\eg, paper, or masks) and are assumed to yield zero spoof cue maps, we first propose an effective language-guided spoof cue map estimation to enhance one-class FAS models by simulating whether the underlying faces are covered by attack-related objects and generating corresponding nonzero spoof cue maps. Next, we introduce a novel prompt-driven liveness feature disentanglement to alleviate live/spoof-irrelative domain variations by disentangling live/spoof-relevant and domain-dependent information. Finally, we design an effective augmentation strategy by fusing latent features from live images and spoof prompts to generate spoof-like image features and thus diversify latent spoof features to facilitate the learning of one-class FAS. Our extensive experiments and ablation studies support that SLIP consistently outperforms previous one-class FAS methods.

Many face anti-spoofing (FAS) methods have focused on learning discriminative features from both live and spoof training data to strengthen the security of face recognition systems. However, since not every possible attack type is available in the training stage, these FAS methods usually fail to detect unseen attacks in the inference stage. In comparison, one-class FAS, where training data comprise only live faces, aims to detect whether a test face image belongs to the live class or not. In this paper, we propose a novel One-Class Spoof Cue Map estimation Network (OC-SCMNet) to address the one-class FAS detection problem. Our first goal is to learn to extract latent spoof features from live images so that their estimated Spoof Cue Maps (SCMs) should have zero responses. To avoid trapping to a trivial solution, we devise a novel SCM-guided feature learning by combining many SCMs as pseudo ground-truths to guide a conditional generator to create latent spoof features for spoof data. Our second goal is to simulate the potential out-of-distribution spoof attacks approximately. To this end, we propose using a memory bank to dynamically preserve a set of sufficiently “independent” latent spoof features to encourage the generator to probe the latent spoof feature space. Extensive experiments conducted on eight FAS benchmark datasets demonstrate that the proposed OC-SCMNet not only outperforms previous one-class FAS approaches but also achieves performance comparable to the state-of-the-art two-class FAS methods. The code is available at https://github.com/Pei-KaiHuang/CVPR24_OC_SCMNet.

The goal of single-domain generalization is to learn a domain- generalized model from only one single source domain. To avoid overfitting to the source domain, recent research focused on domain augmentation for learning domain generalized features. Therefore, domain diversity is indeed crucial to the generalization ability of the model. In this paper, we propose a novel dual-level domain augmentation framework to enrich the domain diversity for single-domain generalized semantic segmentation. We specifically devise an Image-Level and a Class-Level Augmentation Module (IAM and CAM) to enlarge the diversity of augmented images and per-class features, respectively. From the original and augmented data, we then design a Domain-Generalized Feature Learning to learn representative features regularized by a large-scale pretrained model. Experimental results on semantic segmentation benchmarks demonstrate the effectiveness and outperformance of the proposed method over previous work.

Face anti-spoofing (FAS) aims to counter facial presentation attacks and heavily relies on identifying live/spoof discriminative features. While vision transformer (ViT) has shown promising potential in recent FAS methods, there remains a lack of studies examining the values of incorporating local descriptive feature learning with ViT. In this paper, we propose a novel LDCformer by incorporating Learnable Descriptive Convolution (LDC) with ViT and aim to learn distinguishing characteristics of FAS through modeling long-range dependency of locally descriptive features. In addition, we propose to extend LDC to a Decoupled Learnable Descriptive Convolution (Decoupled-LDC) for improving the optimization efficiency. With the new Decoupled-LDC, we further develop an extended model LDCformerDfor FAS. Extensive experiments on FAS benchmarks show that LDCformerDoutperforms previous methods on most of the protocols in both intra-domain and cross-domain testings. The codes are available at https://github.com/Pei-KaiHuang/ICIP23_D-LDCformer.

Face anti-spoofing (FAS) aims to strengthen security of facial identity authentication by distinguishing live faces from spoof ones. Although disentangled feature learning has achieved much success in FAS, the representation capacity of disentangled feature space remains limited and does not extend beyond the training domains. In this paper, we propose to further augment the disentangled liveness and domain features with a two-fold goal. Our first goal is to enrich the diversity of liveness features so as to encompass a wide range of facial representation attacks. The second goal is to expand the domain features toward well-generalized and unseen domains. To reach the two goals, we develop a Disentangled Feature Augmentation Network (DFANet) with two feature augmentation strategies, including Affine Feature Transformation (AFT) and Adversarial Domain Learning (ADL). Extensive experiments on four FAS benchmark datasets show that the proposed DFANet outperforms previous methods on most of the protocols under cross-domain testings. The codes are available at https://github.com/Jxchong1999/DFANet.

Face presentation attack detection methods have been de-veloped to counter presentation attacks and achieved consid-erable success, thanks to large training data and newly developed deep-learning technology. However, when encountering the attacks provided with few training examples, the learning-based detection methods tend to overfit to the small dataset and lead to poor generalization. In this paper, we study this scenario and propose to augment the limited data via disentangled feature learning. We include the live/spoof classifi-cation task and the person identification task in a multi-task learning framework to disentangle the liveness and identity features. To enlarge the number of training samples, we de-sign two remixing strategies on the disentangled features under the identity preservation constraint and the reconstruction constraint, and also adopt the idea of contrastive learning to ensure the discriminability of the augmented samples. Exper-imental results on several benchmark datasets show that the proposed augmentation method significantly improves many detection methods under the limited data scenario.