Iris Presentation Attack Detection (PAD) is critical for securing recognition systems, yet its practical deployment is severely hindered by the poor generalization of models across different acquisition devices and diverse datasets. To address this persistent cross-domain challenge, we first introduce a comprehensive evaluation framework, the Iris Presentation Attack Detection Cross-Domain-Testing (IPAD-CDT) Protocol, designed to evaluate the model robustness in these scenarios. Our core contribution is a novel Masked Mixture-of-Experts (MMoE) method, which enhances the generalization of Transformer-based architectures. MMoE introduces a structured information asymmetry, where "student" Experts learn robust features from masked inputs by distilling knowledge from an unmasked "teacher" Expert via a cosine distance loss. This mask-and-distill mechanism effectively mitigates overfitting and guides the model to learn domain-invariant cues. By integrating MMoE into a CLIP-based model, we conduct extensive experiments on our IPAD-CDT protocol. The results demonstrate that our method sets a new state-of-the-art, significantly outperforming existing models, especially in the challenging cross-dataset and cross-device settings.

Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separately due to their distinct characteristics. Thus, when being combined attacked, almost all methods could not deal. Some studies attempt to combine the sparse data from both types of attacks into a single dataset and try to find a common feature space, which is often impractical due to the space is difficult to be found or even non-existent. To overcome these challenges, we propose a novel approach that uses the sparse model to handle sparse data, utilizing different parameter groups to process distinct regions of the sparse feature space. Specifically, we employ the Mixture of Experts (MoE) framework in our model, expert parameters are matched to tokens with varying weights during training and adaptively activated during testing. However, the traditional MoE struggles with the complex and irregular classification boundaries of this problem. Thus, we introduce a flexible self-adapting weighting mechanism, enabling the model to better fit and adapt. In this paper, we proposed La-SoftMoE CLIP, which allows for more flexible adaptation to the Unified Attack Detection (UAD) task, significantly enhancing the model’s capability to handle diversity attacks. Experiment results demonstrate that our proposed method has SOTA performance.

Face anti-spoofing (FAS) based on domain generalization (DG) has attracted increasing attention from researchers.The reason for the poor generalization is that the model is overfitted to salient liveness-irrelevant signals.However, the previous methods alleviate the overfitting by mapping the images from multiple domains into a common feature space or promoting the separation of image features from domain-specific features and task-related features.If the text features of vision-language pre-trained (VLP) models (e.g., CLIP) are used to dynamically adjust the image features to gain a better generalization, we can not only explore a wider feature space but also avoid the potential degradation of semantic information.Specifically, we propose a FAS method of Style-Conditional Prompt Token Learning (S-CPTL), which aims to generate generalized text features by training the introduced prompt tokens to carry visual styles and use them as weights for classifiers to improve the model's generalization.Compared to the inherently static prompt token, we propose the dynamic prompt token, which can adaptively capture live-irrelevant signals from the instance-specific styles and increase their diversity through mixed feature statistics to further reduce the overfitting of the model.Thorough experimental analysis demonstrates that S-CPTL exceeds current top-performing methods in four distinct cross-dataset benchmarks.

There has been an increasing focus on domain-generalized (DG) face anti-spoofing (FAS). However, existing methods aim to project a shared visual space through adversarial training, making exploring the space without losing semantic information challenging. We investigate the DG inadequacies resulting from classifier overfitting to a significantly different domain distribution. To address this issue, we propose a novel Fine-Grained Prompt Learning (FGPL) based on Vision-Language Models (VLMs), such as CLIP, which can adaptively adjust weights for classifiers with text features to mitigate overfitting. Specifically, FGPL first motivates the prompts to learn content and domain semantic information by capturing Domain-Agnostic and Domain-Specific features. Furthermore, our prompts are designed to be category-generalized by diversifying the Domain-Specific prompts. Additionally, we design an Adaptive Convolutional Adapter (AC-adapter), which is implemented through an adaptive combination of Vanilla Convolution and Central Difference Convolution, to be inserted into the image encoder for quickly bridging the gap between general image recognition and FAS task. Extensive experiments demonstrate that the proposed FGPL is effective and outperforms state-of-the-art methods on several cross-domain datasets.

With the development of iris recognition, many identity authentication applications began to use this inherent biometric ID. Despite the breakthroughs in the identification with iris recognition technology, one primary problem remains unsolved: the presentation spoof attack. In this paper, we present a novel algorithm 4DCycle-GAN for expanding the spoof iris image database by synthesizing fake iris images wearing textured contact lenses. The proposed 4DCycle-GAN follows the Cycle-Consistent Adversarial Networks (Cycle-GAN) framework which translating between one kind images (genuine iris images) and one other kind images (textured contact lenses iris images). The 4DCycle-GAN introduces two more discriminators to improve the Cycle-GAN at the defect of lack of diversity. The two new discriminators `prefer' images generated by the generators, while the original discriminators in Cycle-GAN `prefer' real captured images. These new added confrontations make the 4DCycle-GAN avoid generating a certain kind of contact lenses texture which is larger percentage of the training iris database. The synthesized textured contact lenses iris images are used for spoofing iris detection training to improve the robustness of classification algorithm. Both the Cycle-GAN and the 4DCycle-GAN synthesizing images can improve the spoof classification results. Moreover, by using the 4DCycle-GAN, the spoof classification results are distinctly improved for unrelated non-homologous database experiments. Extensive experimental results show that the proposed method can improve the anti-spoof ability of iris recognition system.