Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images

• URL: http://arxiv.org/abs/2505.03611v1
• Date: Tue, 06 May 2025 15:09:37 GMT
• Title: Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images
• Authors: Fangling Jiang, Qi Li, Weining Wang, Wei Shen, Bing Liu, Zhenan Sun,
• Abstract summary: We propose a novel approach for learning unknown spoof prompts, relying solely on real face images from a single source domain.<n>Our method generates textual prompts for real faces and potential unknown spoof attacks by leveraging the general knowledge embedded in vision-language models.
• Score: 46.658223044347
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Face anti-spoofing is a critical technology for ensuring the security of face recognition systems. However, its ability to generalize across diverse scenarios remains a significant challenge. In this paper, we attribute the limited generalization ability to two key factors: covariate shift, which arises from external data collection variations, and semantic shift, which results from substantial differences in emerging attack types. To address both challenges, we propose a novel approach for learning unknown spoof prompts, relying solely on real face images from a single source domain. Our method generates textual prompts for real faces and potential unknown spoof attacks by leveraging the general knowledge embedded in vision-language models, thereby enhancing the model's ability to generalize to unseen target domains. Specifically, we introduce a diverse spoof prompt optimization framework to learn effective prompts. This framework constrains unknown spoof prompts within a relaxed prior knowledge space while maximizing their distance from real face images. Moreover, it enforces semantic independence among different spoof prompts to capture a broad range of spoof patterns. Experimental results on nine datasets demonstrate that the learned prompts effectively transfer the knowledge of vision-language models, enabling state-of-the-art generalization ability against diverse unknown attack types across unseen target domains without using any spoof face images.

Related papers

• Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection [84.21257150497254]
  We propose a novel knowledge-based prompt learning framework to explore the strong generalization capability of vision-language models for 3D mask presentation attack detection.<n> Experimental results demonstrate that the proposed method achieves state-of-the-art intra- and cross-scenario detection performance.
  arXiv  Detail & Related papers  (2025-05-06T15:09:23Z)
• Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models [32.23201683108716]
  We propose a novel strategy that exclusively employs image patches for attacks, thus preserving the integrity of the original text. Our method leverages prior knowledge from diffusion models to enhance the authenticity and naturalness of the perturbations. Comprehensive experiments conducted in a white-box setting for image-to-text scenarios reveal that our proposed method significantly outperforms existing techniques, achieving a 100% attack success rate.
  arXiv  Detail & Related papers  (2024-10-07T10:06:01Z)
• Transferable Adversarial Facial Images for Privacy Protection [15.211743719312613]
  We present a novel face privacy protection scheme with improved transferability while maintain high visual quality. We first exploit global adversarial latent search to traverse the latent space of the generative model. We then introduce a key landmark regularization module to preserve the visual identity information.
  arXiv  Detail & Related papers  (2024-07-18T02:16:11Z)
• Imperceptible Face Forgery Attack via Adversarial Semantic Mask [59.23247545399068]
  We propose an Adversarial Semantic Mask Attack framework (ASMA) which can generate adversarial examples with good transferability and invisibility. Specifically, we propose a novel adversarial semantic mask generative model, which can constrain generated perturbations in local semantic regions for good stealthiness.
  arXiv  Detail & Related papers  (2024-06-16T10:38:11Z)
• Anonymization Prompt Learning for Facial Privacy-Preserving Text-to-Image Generation [56.46932751058042]
  We train a learnable prompt prefix for text-to-image diffusion models, which forces the model to generate anonymized facial identities. Experiments demonstrate the successful anonymization performance of APL, which anonymizes any specific individuals without compromising the quality of non-identity-specific image generation.
  arXiv  Detail & Related papers  (2024-05-27T07:38:26Z)
• Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts [23.279652897139286]
  Highly realistic AI generated face forgeries known as deepfakes have raised serious social concerns. We provide counterfactual explanations for face forgery detection from an artifact removal perspective. Our method achieves over 90% attack success rate and superior attack transferability.
  arXiv  Detail & Related papers  (2024-04-12T09:13:37Z)
• Generalized Face Liveness Detection via De-fake Face Generator [52.23271636362843]
  Previous Face Anti-spoofing (FAS) methods face the challenge of generalizing to unseen domains.<n>We propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces.<n>Our method achieves state-of-the-art results under cross-domain evaluations with unseen scenarios and unknown presentation attacks.
  arXiv  Detail & Related papers  (2024-01-17T06:59:32Z)
• Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework [20.652130361862053]
  We propose the Adversarial Decoupling Augmentation Framework (ADAF) to enhance the defensive performance of facial privacy protection algorithms. ADAF introduces multi-level text-related augmentations for defense stability against various attacker prompts.
  arXiv  Detail & Related papers  (2023-05-06T09:00:50Z)
• Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing [25.07432145233952]
  Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones. It is not practical to assume that the type of spoof attacks would be known in advance. We propose a deep learning model for addressing the aforementioned domain-generalized face anti-spoofing task.
  arXiv  Detail & Related papers  (2022-08-16T16:13:24Z)
• Restricted Black-box Adversarial Attack Against DeepFake Face Swapping [70.82017781235535]
  We introduce a practical adversarial attack that does not require any queries to the facial image forgery model. Our method is built on a substitute model persuing for face reconstruction and then transfers adversarial examples from the substitute model directly to inaccessible black-box DeepFake models.
  arXiv  Detail & Related papers  (2022-04-26T14:36:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.