mlsanityData to 14 Oct 2026

InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing

arXiv 2507.12060 ↗First posted 16 Jul 2025cs.CV cs.AI cs.MMcited by 3

On the map: Multimodal LLMs / vision-language models → MLLM jailbreaks and adversarial robustness

Abstract (arXiv)

Face anti-spoofing (FAS) aims to construct a robust system that can withstand diverse attacks. While recent efforts have concentrated mainly on cross-domain generalization, two significant challenges persist: limited semantic understanding of attack types and training redundancy across domains. We address the first by integrating vision-language models (VLMs) to enhance the perception of visual input. For the second challenge, we employ a meta-domain strategy to learn a unified model that generalizes well across multiple domains. Our proposed InstructFLIP is a novel instruction-tuned framework that leverages VLMs to enhance generalization via textual guidance trained solely on a single domain. At its core, InstructFLIP explicitly decouples instructions into content and style components, where content-based instructions focus on the essential semantics of spoofing, and style-based instructions consider variations related to the environment and camera characteristics. Extensive experiments demonstrate the effectiveness of InstructFLIP by outperforming SOTA models in accuracy and substantially reducing training redundancy across diverse domains in FAS. Project website is available at https://kunkunlin1221.github.io/InstructFLIP.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 33 of its 52 references that are in the map. First posted on arXiv 16 Jul 2025.

Builds on

33 of 52 references.

References · 33

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  3. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,876
  4. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  5. 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,433
  6. 2021Open-vocabulary Object Detection via Vision and Language Knowledge Distillationcited by 906
  7. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
  8. 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
  9. 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
  10. 2020Single-Side Domain Generalization for Face Anti-Spoofingcited by 139
  11. 2020CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotationscited by 109
  12. 2020Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learningcited by 93
21 more
  1. 2014Learn Convolutional Neural Network for Face Anti-Spoofingcited by 80
  2. 2019Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Networkcited by 79
  3. 2022Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofingcited by 73
  4. 2022PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognitioncited by 67
  5. 2020CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofingcited by 66
  6. 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 65
  7. 2019CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofingcited by 61
  8. 2018Face De-Spoofing: Anti-Spoofing via Noise Modelingcited by 60
  9. 2018A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofingcited by 51
  10. 2023Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignmentcited by 48
  11. 2023FLIP: Cross-domain Face Anti-spoofing with Language Guidancecited by 45
  12. 2023Instance-Aware Domain Generalization for Face Anti-Spoofingcited by 42
  13. 2024CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofingcited by 39
  14. 2022Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofingcited by 28
  15. 2020Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Reviewcited by 25
  16. 2020Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofingcited by 14
  17. 2021Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofingcited by 10
  18. 2022Cyclically Disentangled Feature Translation for Face Anti-spoofingcited by 10
  19. 2024La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detectioncited by 8
  20. 2024DiffFAS: Face Anti-Spoofing via Generative Diffusion Modelscited by 7
  21. 2025Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detectioncited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmarkcited by 1
  2. 2026Employing Vision-Language Models for Face Image Quality Assessmentcited by 0
  3. 2026Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing
  2. 2025FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models
  3. 2025Exploring Task-Solving Paradigm for Generalized Cross-Domain Face Anti-Spoofing via Reinforcement Fine-Tuning
  4. 2025SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
  5. 2025Steering Vision-Language Pre-trained Models for Incremental Face Presentation Attack Detection
  6. 2025Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models
  7. 2025Harnessing Chain-of-Thought Reasoning in Multimodal Large Language Models for Face Anti-Spoofing
  8. 2025Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images