# FLIP: Cross-domain Face Anti-spoofing with Language Guidance

arXiv: https://arxiv.org/abs/2309.16649 · HTML: https://mlsanity.com/paper/2309.16649/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 69 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 30 of 53)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification](https://mlsanity.com/paper/2207.09519/) (2022)
- [Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers](https://mlsanity.com/paper/2103.15679/) (2021)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection](https://mlsanity.com/paper/2207.03482/) (2022)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://mlsanity.com/paper/2011.02062/) (2020)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [Regularized Fine-grained Meta Face Anti-spoofing](https://mlsanity.com/paper/1911.10771/) (2019)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)

## Built on by (most cited first, 30 of 45)

- [SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2402.04178/) (2024)
- [CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing](https://mlsanity.com/paper/2403.14333/) (2024)
- [FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning](https://mlsanity.com/paper/2412.12032/) (2024)
- [DeCLIP: Decoding CLIP representations for deepfake localization](https://mlsanity.com/paper/2409.08849/) (2024)
- [Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis](https://mlsanity.com/paper/2409.03501/) (2024)
- [FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection](https://mlsanity.com/paper/2501.02892/) (2025)
- [Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning](https://mlsanity.com/paper/2501.08799/) (2025)
- [Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues](https://mlsanity.com/paper/2404.08450/) (2024)
- [La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detection](https://mlsanity.com/paper/2408.12793/) (2024)
- [Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering](https://mlsanity.com/paper/2504.04470/) (2025)
- [FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models](https://mlsanity.com/paper/2505.09415/) (2025)
- [SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining](https://mlsanity.com/paper/2503.19982/) (2025)
- [Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning](https://mlsanity.com/paper/2505.13327/) (2025)
- [Harnessing Chain-of-Thought Reasoning in Multimodal Large Language Models for Face Anti-Spoofing](https://mlsanity.com/paper/2506.01783/) (2025)
- [Generalized Face Liveness Detection via De-fake Face Generator](https://mlsanity.com/paper/2401.09006/) (2024)
- [DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing](https://mlsanity.com/paper/2503.00429/) (2025)
- [Empowering Morphing Attack Detection using Interpretable Image-Text Foundation Model](https://mlsanity.com/paper/2508.10110/) (2025)
- [Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2604.19196/) (2026)
- [Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing](https://mlsanity.com/paper/2404.14406/) (2024)
- [InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing](https://mlsanity.com/paper/2507.12060/) (2025)
- [TeG-DG: Textually Guided Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2311.18420/) (2023)
- [Principles of Designing Robust Remote Face Anti-Spoofing Systems](https://mlsanity.com/paper/2406.03684/) (2024)
- [Multi-Granularity Language-Guided Training for Multi-Object Tracking](https://mlsanity.com/paper/2406.04844/) (2024)
- [Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems](https://mlsanity.com/paper/2407.08514/) (2024)
- [STAR: A Foundation Model-driven Framework for Robust Task Planning and Failure Recovery in Robotic Systems](https://mlsanity.com/paper/2503.06060/) (2025)
- [Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection](https://mlsanity.com/paper/2505.03610/) (2025)
- [Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images](https://mlsanity.com/paper/2505.03611/) (2025)
- [Exploring Task-Solving Paradigm for Generalized Cross-Domain Face Anti-Spoofing via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.21895/) (2025)
- [Group-wise Scaling and Orthogonal Decomposition for Domain-Invariant Feature Extraction in Face Anti-Spoofing](https://mlsanity.com/paper/2507.04006/) (2025)
- [Multi-View Slot Attention Using Paraphrased Texts for Face Anti-Spoofing](https://mlsanity.com/paper/2509.06336/) (2025)

## Abstract (arXiv)

Face anti-spoofing (FAS) or presentation attack detection is an essential component of face recognition systems deployed in security-critical applications. Existing FAS methods have poor generalizability to unseen spoof types, camera sensors, and environmental conditions. Recently, vision transformer (ViT) models have been shown to be effective for the FAS task due to their ability to capture long-range dependencies among image patches. However, adaptive modules or auxiliary loss functions are often required to adapt pre-trained ViT weights learned on large-scale datasets such as ImageNet. In this work, we first show that initializing ViTs with multimodal (e.g., CLIP) pre-trained weights improves generalizability for the FAS task, which is in line with the zero-shot transfer capabilities of vision-language pre-trained (VLP) models. We then propose a novel approach for robust cross-domain FAS by grounding visual representations with the help of natural language. Specifically, we show that aligning the image representation with an ensemble of class descriptions (based on natural language semantics) improves FAS generalizability in low-data regimes. Finally, we propose a multimodal contrastive learning strategy to boost feature generalization further and bridge the gap between source and target domains. Extensive experiments on three standard protocols demonstrate that our method significantly outperforms the state-of-the-art methods, achieving better zero-shot transfer performance than five-shot transfer of adaptive ViTs. Code: https://github.com/koushiksrivats/FLIP