# Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era

arXiv: https://arxiv.org/abs/2202.10673 · HTML: https://mlsanity.com/paper/2202.10673/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://mlsanity.com/paper/1912.13457/) (2019)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [X2Face: A network for controlling face generation by using images, audio, and pose codes](https://mlsanity.com/paper/1807.10550/) (2018)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [face anti-spoofing based on color texture analysis](https://mlsanity.com/paper/1511.06316/) (2015)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection](https://mlsanity.com/paper/1907.04047/) (2019)
- [Deep Tree Learning for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/1904.02860/) (2019)
- [Face De-Spoofing: Anti-Spoofing via Noise Modeling](https://mlsanity.com/paper/1807.09968/) (2018)
- [ICface: Interpretable and Controllable Face Reenactment Using GANs](https://mlsanity.com/paper/1904.01909/) (2019)
- [Adversarial CAPTCHAs](https://mlsanity.com/paper/1901.01107/) (2019)
- [Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections](https://mlsanity.com/paper/1801.01949/) (2018)
- [The Emerging Threats of Deepfake Attacks and Countermeasures](https://mlsanity.com/paper/2012.07989/) (2020)

## Built on by (most cited first, 27 of 27)

- [IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI](https://mlsanity.com/paper/2310.19248/) (2023)
- [Model Extraction Attacks Revisited](https://mlsanity.com/paper/2312.05386/) (2023)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape](https://mlsanity.com/paper/2404.16212/) (2024)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [Why Do Facial Deepfake Detectors Fail?](https://mlsanity.com/paper/2302.13156/) (2023)
- [Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings](https://mlsanity.com/paper/2204.04063/) (2022)
- ["Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution](https://mlsanity.com/paper/2209.01782/) (2022)
- [GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response](https://mlsanity.com/paper/2210.06186/) (2022)
- [CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models](https://mlsanity.com/paper/2502.07225/) (2025)
- [FaceSwapGuard: Safeguarding Facial Privacy from DeepFake Threats through Identity Obfuscation](https://mlsanity.com/paper/2502.10801/) (2025)
- [LLMs Are Not Yet Ready for Deepfake Image Detection](https://mlsanity.com/paper/2506.10474/) (2025)
- [How Generalizable are Deepfake Image Detectors? An Empirical Study](https://mlsanity.com/paper/2308.04177/) (2023)
- [Swap It Like Its Hot: Segmentation-based spoof attacks on eye-tracking images](https://mlsanity.com/paper/2404.13827/) (2024)
- [Principles of Designing Robust Remote Face Anti-Spoofing Systems](https://mlsanity.com/paper/2406.03684/) (2024)
- [PromptFlare: Prompt-Generalized Defense via Cross-Attention Decoy in Diffusion-Based Inpainting](https://mlsanity.com/paper/2508.16217/) (2025)
- [Robust Person Identification: A WiFi Vision-based Approach](https://mlsanity.com/paper/2210.00127/) (2022)
- [FakeSwarm: Improving Fake News Detection with Swarming Characteristics](https://mlsanity.com/paper/2305.19194/) (2023)
- [Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification](https://mlsanity.com/paper/2605.00218/) (2026)
- [Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models](https://mlsanity.com/paper/2303.13131/) (2023)
- [Beyond Known Fakes: Generalized Detection of AI-Generated Images via Post-hoc Distribution Alignment](https://mlsanity.com/paper/2502.10803/) (2025)
- [Spotting tell-tale visual artifacts in face swapping videos: strengths and pitfalls of CNN detectors](https://mlsanity.com/paper/2506.16497/) (2025)
- [ID-Eraser: Proactive Defense Against Face Swapping via Identity Perturbation](https://mlsanity.com/paper/2604.21465/) (2026)
- [The End of Trust: How Agentic AI Breaks Security Assumptions](https://mlsanity.com/paper/2605.16436/) (2026)
- [IMFD: End-to-end Multi-Face Forgery Detection through Instruction-based Large Vision-Language Models](https://mlsanity.com/paper/2609.19693/) (2026)
- [Not All Challenges Are Equal: Evaluating Interactive Liveness Detection Against Real-Time Face Swaps](https://mlsanity.com/paper/2610.16417/) (2026)

## Abstract (arXiv)

Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors. Yet, with the rapid advances in synthetic media techniques (e.g., deepfake), the security of FLV is facing unprecedented challenges, about which little is known thus far. To bridge this gap, in this paper, we conduct the first systematic study on the security of FLV in real-world settings. Specifically, we present LiveBugger, a new deepfake-powered attack framework that enables customizable, automated security evaluation of FLV. Leveraging LiveBugger, we perform a comprehensive empirical assessment of representative FLV platforms, leading to a set of interesting findings. For instance, most FLV APIs do not use anti-deepfake detection; even for those with such defenses, their effectiveness is concerning (e.g., it may detect high-quality synthesized videos but fail to detect low-quality ones). We then conduct an in-depth analysis of the factors impacting the attack performance of LiveBugger: a) the bias (e.g., gender or race) in FLV can be exploited to select victims; b) adversarial training makes deepfake more effective to bypass FLV; c) the input quality has a varying influence on different deepfake techniques to bypass FLV. Based on these findings, we propose a customized, two-stage approach that can boost the attack success rate by up to 70%. Further, we run proof-of-concept attacks on several representative applications of FLV (i.e., the clients of FLV APIs) to illustrate the practical implications: due to the vulnerability of the APIs, many downstream applications are vulnerable to deepfake. Finally, we discuss potential countermeasures to improve the security of FLV. Our findings have been confirmed by the corresponding vendors.