# Unified Physical-Digital Face Attack Detection

arXiv: https://arxiv.org/abs/2401.17699 · HTML: https://mlsanity.com/paper/2401.17699/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 58 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2203.06605/) (2022)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing](https://mlsanity.com/paper/1904.09709/) (2019)
- [NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing](https://mlsanity.com/paper/2011.02062/) (2020)
- [SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing](https://mlsanity.com/paper/1906.07927/) (2019)
- [Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity](https://mlsanity.com/paper/2203.05151/) (2022)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)
- [Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing](https://mlsanity.com/paper/2203.05340/) (2022)
- [CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing](https://mlsanity.com/paper/2003.05136/) (2020)
- [AdvFaces: Adversarial Face Synthesis](https://mlsanity.com/paper/1908.05008/) (2019)
- [AdvDrop: Adversarial Attack to DNNs by Dropping Information](https://mlsanity.com/paper/2108.09034/) (2021)
- [FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping](https://mlsanity.com/paper/2210.10473/) (2022)

## Built on by (most cited first, 17 of 17)

- [Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues](https://mlsanity.com/paper/2404.08450/) (2024)
- [Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection](https://mlsanity.com/paper/2504.00458/) (2025)
- [Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning](https://mlsanity.com/paper/2505.13327/) (2025)
- [Unified Physical-Digital Attack Detection Challenge](https://mlsanity.com/paper/2404.06211/) (2024)
- [FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection](https://mlsanity.com/paper/2504.00454/) (2025)
- [Principles of Designing Robust Remote Face Anti-Spoofing Systems](https://mlsanity.com/paper/2406.03684/) (2024)
- [SUEDE:Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement](https://mlsanity.com/paper/2504.04818/) (2025)
- [Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation](https://mlsanity.com/paper/2505.18787/) (2025)
- [Multi-View Slot Attention Using Paraphrased Texts for Face Anti-Spoofing](https://mlsanity.com/paper/2509.06336/) (2025)
- [Optimizing DINOv2 with Registers for Face Anti-Spoofing](https://mlsanity.com/paper/2510.17201/) (2025)
- [A Unified Framework for Iris Anti-Spoofing: Introducing Iris Anti-Spoofing Cross-Domain-Testing Protocol and Masked-MoE Method](https://mlsanity.com/paper/2408.09752/) (2024)
- [Unified Face Matching and Physical-Digital Spoofing Attack Detection](https://mlsanity.com/paper/2501.09635/) (2025)
- [Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection](https://mlsanity.com/paper/2512.06363/) (2025)
- [Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization](https://mlsanity.com/paper/2603.00266/) (2026)
- [UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning](https://mlsanity.com/paper/2605.08709/) (2026)
- [Unified Face Attack Detection via Fine-Grained Semantic Guidance](https://mlsanity.com/paper/2607.08156/) (2026)
- [MS-MFAD : Multimodal large language models for Face Anti-spoofing Detection](https://mlsanity.com/paper/2608.17328/) (2026)

## Abstract (arXiv)

Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus more computational burden. The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks with ID consistency which means the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. The dataset consists of 1,800 participations of 2 and 12 physical and digital attacks, respectively, resulting in a total of 29,706 videos. Then, we propose a Unified Attack Detection framework based on Vision-Language Models (VLMs), namely UniAttackDetection, which includes three main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring unified and specific knowledge respectively; the Unified Knowledge Mining (UKM) module, designed to capture a comprehensive feature space; and the Sample-Level Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics. These three modules seamlessly form a robust unified attack detection framework. Extensive experiments on UniAttackData and three other datasets demonstrate the superiority of our approach for unified face attack detection.