Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing

• URL: http://arxiv.org/abs/2608.17351v2
• Date: Mon, 24 Aug 2026 04:12:03 GMT
• Title: Primitive-Driven Compositional Forensic Visual Prompting for Open-World Face Anti-Spoofing
• Abstract summary: We propose a compositional visual prompt learning framework that operates entirely in the visual feature space.<n>The framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units.<n>Experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.
• Score: 59.765442274924595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-world face anti-spoofing must address both covariate and semantic shifts: source and target domains differ in imaging conditions, while target domains contain diverse attack types absent from training. Existing prompt-based approaches often express spoofing through category semantics or language guidance, which is effective for modeling high-level concepts but is less suited to explicitly capturing the evolving fine-grained and spatially heterogeneous forensic evidence of unseen attacks. Motivated by the hypothesis that many unseen attacks can be characterized by new combinations of recurring visual cues, we propose a compositional forensic visual prompt learning framework that operates entirely in the visual feature space. Built on a frozen ViT-based vision foundation model, the framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units derived from image patches. Class-specific global contextual prompts then provide input-dependent routing weights that adaptively select and compose these primitives into compositional forensic visual prompts for real/spoof discrimination. The primitives are not assigned predefined semantic meanings; instead, their specialization and reuse emerge from shared parameterization and joint optimization across categories. Extensive experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.

Related papers

• Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models [57.357091028792325]
  Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment.<n>We propose a novel constrained prompt enhancement (CPE) method to improve visual-textual alignment.<n>Our approach consists of two key components: Topology-Guided Synonymous Semantic Generation (TGSSG) and Category-Agnostic Discriminative Region Selection (CADRS)
  arXiv  Detail & Related papers  (2025-08-24T15:45:22Z)
• HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning Embeddings Transformation for Face Forgery Detection [6.060036926093259]
  HAMLET-FFD is a cross-domain generalization framework for face forgery detection.<n>It integrates visual evidence with conceptual cues, emulating expert forensic analysis.<n>By design, HAMLET-FFD freezes all pretrained parameters, serving as an external plugin.
  arXiv  Detail & Related papers  (2025-07-28T15:09:52Z)
• Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning [58.16354555208417]
  PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively.<n>The lack of a Unified Face Attack Detection model to simultaneously handle attacks in these two categories is mainly attributed to two factors.<n>We present a novel Visual-Language Model-based Hierarchical Prompt Tuning Framework that adaptively explores multiple classification criteria from different semantic spaces.
  arXiv  Detail & Related papers  (2025-05-19T16:35:45Z)
• Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning [58.73625654718187]
  Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes.<n>Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features.<n>This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation.
  arXiv  Detail & Related papers  (2025-03-29T10:17:57Z)
• Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification [49.41632476658246]
  We discuss the extension of DFKD to Vision-Language Foundation Models without access to the billion-level image-text datasets. The objective is to customize a student model for distribution-agnostic downstream tasks with given category concepts. We propose three novel Prompt Diversification methods to encourage image synthesis with diverse styles.
  arXiv  Detail & Related papers  (2024-07-21T13:26:30Z)
• Beyond Mask: Rethinking Guidance Types in Few-shot Segmentation [67.35274834837064]
  We develop a universal vision-language framework (UniFSS) to integrate prompts from text, mask, box, and image. UniFSS significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-16T08:41:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.