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.

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.

In recent years, many video tasks have achieved breakthroughs by utilizing the vision transformer and establishing spatial-temporal decoupling for feature extraction. Although multi-view 3D reconstruction also faces multiple images as input, it cannot immediately inherit their success due to completely ambiguous associations between unstructured views. There is not usable prior relationship, which is similar to the temporally-coherence property in a video. To solve this problem, we propose a novel transformer network for Unstructured Multiple Images (UMIFormer). It exploits transformer blocks for decoupled intra-view encoding and designed blocks for token rectification that mine the correlation between similar tokens from different views to achieve decoupled interview encoding. Afterward, all tokens acquired from various branches are compressed into a fixed-size compact representation while preserving rich information for reconstruction by leveraging the similarities between tokens. We empirically demonstrate on ShapeNet and confirm that our decoupled learning method is adaptable for unstructured multiple images. Meanwhile, the experiments also verify our model outperforms existing SOTA methods by a large margin. Code will be available at https://github.com/GaryZhu1996/UMIFormer.