In this work, borrowing a solution from the large-scale vision-language models (VLMs) instead of directly removing modality-specific signals from visual features, we propose a novel Flexible Modal CLIP (FM-CLIP) for flexible modal FAS, that can utilize text features to dynamically adjust visual features to be modality independent. In the visual branch, considering the huge visual differences of the same attack in different modalities, which makes it difficult for classifiers to flexibly identify subtle spoofing clues in different test modalities, we propose Cross-Modal Spoofing Enhancer (CMS-Enhancer). It includes a Frequency Extractor (FE) and Cross-Modal Interactor (CMI), aiming to map different modal attacks in a shared frequency space to reduce interference from modality-specific signals and enhance spoofing clues by leveraging cross-modal learning from the shared frequency space. In the text branch, we introduce a Language-Guided Patch Alignment (LGPA) based on prompt learning, which further guides the image encoder to focus on patch-level spoofing representations through dynamic weighting by text features. Thus, our FM-CLIP can flexibly test different modal samples by identifying and enhancing modality-agnostic spoofing cues. Finally, extensive experiments show that FM-CLIP is effective and outperforms state-of-the-art methods on multiple multi-modal datasets.

The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-based algorithms have been developed to alleviate this interference by adjusting the weights of visual classifiers. How-ever, our analysis of this class-wise prompt engineering suffers from two shortcomings for DG FAS: (1) The categories of facial categories, such as real or spoof, have no semantics for the CLIP model, making it difficult to learn accurate category descriptions. (2) A single form of prompt cannot portray the various types of spoofing. In this work, instead of class-wise prompts, we propose a novel Content-aware Composite Prompt Engineering (CCPE) that generates instance-wise composite prompts, including both fixed template and learnable prompts. Specifically, our CCPE constructs content-aware prompts from two branches: (1) Inherent content prompt explicitly benefits from abundant transferred knowledge from the instruction-based Large Language Model (LLM). (2) Learnable content prompts implicitly extract the most informative visual content via Q-Former. Moreover, we design a Cross-Modal Guidance Module (CGM) that dynamically adjusts unimodal features for fusion to achieve better generalized FAS. Finally, our CCPE has been validated for its effectiveness in multiple cross-domain experiments and achieves state-of-the-art (SOTA) results.

Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical and digital attacks due to: 1) significant intra-class variations between these attack types, and 2) the inadequacy of spatial information alone to comprehensively capture live and fake cues. To address these issues, we propose a unified attack detection model termed Frequency-Aware and Attack-Agnostic CLIP (FA³-CLIP), which introduces attack-agnostic prompt learning to express generic live and fake cues derived from the fusion of spatial and frequency features, enabling unified detection of live faces and all categories of attacks. Specifically, the attack-agnostic prompt module generates generic live and fake prompts within the language branch to extract corresponding generic representations from both live and fake faces, guiding the model to learn a unified feature space for unified attack detection. Meanwhile, the module adaptively generates the live/fake conditional bias from the original spatial and frequency information to optimize the generic prompts accordingly, reducing the impact of intra-class variations. We further propose a dual-stream cues fusion framework in the vision branch, which leverages frequency information to complement subtle cues that are difficult to capture in the spatial domain. In addition, a frequency compression block is utilized in the frequency stream, which reduces redundancy in frequency features while preserving the diversity of crucial cues. We also establish new challenging protocols to facilitate unified face attack detection effectiveness. Experimental results on multiple benchmarks demonstrate that FA³-CLIP significantly improves performance, reducing ACER by over 1.2% on UniAttackData, and increasing AUC by more than 3% as well as reducing EER by over 4% on the JFSFDB dataset. The source code is available at https://github.com/YongzeLi/FA3-CLIP

Knowledge distillation has emerged as a primary solution for anomaly detection, leveraging feature discrepancies between teacher–student (T–S) networks to locate anomalies. However, previous approaches suffer from ambiguous feature discrepancies, which hinder effective anomaly detection due to two main challenges: 1) overgeneralization, where the student network excessively mimics teacher features in anomalous regions, and 2) semantic bias between T–S networks in normal regions. To address these issues, we propose an Adaptive Discrepancy Optimization (Ado) block. The Ado block adaptively calibrates feature discrepancies by reducing overgeneralization in anomalous regions and selectively aligning semantic features in normal regions via learnable feature offsets. This versatile block can be seamlessly integrated into various distillation-based methods. Experimental results demonstrate that the Ado block significantly enhances performance across 11 different knowledge distillation frameworks on two widely used datasets. Notably, when integrated with the Ado block, RD4AD achieves a 22% relative improvement in pixel-level PRO on the VisA dataset. In addition, a real-world keyboard inspection application further validates the effectiveness of the Ado block.

Hyperspectral image (HSI) classification requires models that can simultaneously capture spatial structures and spectral continuity. Although state space models (SSMs), particularly Mamba, have shown strong capability in long-sequence modeling, their application to HSI remains limited due to insufficient spectral relation modeling and the constraints of unidirectional processing. To address these challenges, we propose BiMambaHSI, a novel bidirectional spectral—spatial framework. First, we proposed a joint spectral—spatial gated mamba (JGM) encoder that applies forward–backward state modeling with input-dependent gating, explicitly capturing bidirectional spectral—spatial dependencies. This bidirectional mechanism explicitly captures long-range spectral—spatial dependencies, overcoming the limitations of conventional unidirectional Mamba. Second, we introduced the spatial—spectral mamba block (SSMB), which employs parallel bidirectional branches to extract spatial and spectral features separately and integrates them through a lightweight adaptive fusion mechanism. This design enhanced spectral continuity, spatial discrimination, and cross-dimensional interactions while preserving the linear complexity of pure SSMs. Extensive experiments on five public benchmark datasets (Pavia University, Houston, Indian Pines, WHU-Hi-HanChuan, and WHU-Hi-LongKou) demonstrate that BiMambaHSI consistently achieves state-of-the-art performance, improving classification accuracy and robustness compared with existing CNN- and Transformer-based methods.