Red Bleed: A Pragmatic Near-Infrared Presentation Attack on Facial Biometric Authentication Systems
Bowen Hu, Kuo Wang, Chip-Hong Chang
摘要
Facial recognition is the most prevalent biometric modality in commercial verification and identification systems (e.g. Windows Hello and Apple FaceID), which typically operate under near-infrared (NIR) illumination. Such systems are generally considered secure on the premise that no commercial screen display can readily enable a NIR-based video presentation attack. However, this work demonstrates a critical vulnerability of NIR biometric authentication systems by a presentation attack, named Red Bleed, mounted on a widely used commercial-off-the-shelf (COTS) enterprise-grade face authentication system through a custom-built liquid crystal display (LCD) that costs less than 400 USD. Due to the scarcity of NIR video samples, it is more feasible to sneak RGB images in the visible (VIS) spectrum through, for instance, covert secret photography, photos posted on social media or screen captures during video conferencing. Besides using live captured NIR video of the target subject's face, we also propose a novel identity-preserved NIR face generative framework that combines a Variational Autoencoder (VAE) to convert VIS images into the NIR domain for this attack. In conjunction with an advanced face swapping technique, an RGB video can be transformed into a video with NIR face, enabling a more sneaky and pragmatic 2D presentation attack on NIR face biometric authentication demonstrated on a commercially available Windows Hello face authentication module. The hardware design and source code supporting our findings will be made publicly available at https://github.com following paper acceptance and the corresponding Common Vulnerabilities and Exposures (CVE) release. This vulnerability has been reported to Microsoft and the vendors of the three evaluated COTS Windows Hello face recognition modules. The reported behavior has been confirmed by the Microsoft Security Response Center (MSRC), and a CVE is scheduled for public disclosure in June 2025.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper34
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 被引用 13,211 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
相关 Paper
- Spoofing Real-world Face Authentication Systems through Optical SynthesisYueli Yan, Zhice YangS&P 2023
- The Invisible Polyjuice Potion: an Effective Physical Adversarial Attack against Face RecognitionYe Wang, Zeyan Liu, Bo Luo, Rongqing Hui 等CCS 2024 · 被引用 2 次
- Physically-Based Face Rendering for NIR-VIS Face RecognitionYunqi Miao, Alexandros Lattas, Jiankang Deng, Jungong Han 等NeurIPS 2022 · 被引用 11 次
- Virtual U: Defeating Face Liveness Detection by Building Virtual Models from Your Public PhotosYi Xu, True Price, Jan-Michael Frahm, Fabian MonroseUSENIX Security 2016 · 被引用 94 次
- Am I a Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs under Deepfake Impersonation AttackShahroz Tariq, Sowon Jeon, Simon S. WooWWW 2022 · 被引用 33 次
