Exploring Task-Solving Paradigm for Generalized Cross-Domain Face Anti-Spoofing via Reinforcement Fine-Tuning

• URL: http://arxiv.org/abs/2506.21895v1
• Date: Fri, 27 Jun 2025 04:28:29 GMT
• Title: Exploring Task-Solving Paradigm for Generalized Cross-Domain Face Anti-Spoofing via Reinforcement Fine-Tuning
• Authors: Fangling Jiang, Qi Li, Weining Wang, Gang Wang, Bing Liu, Zhenan Sun,
• Abstract summary: This paper presents a reinforcement fine-tuning-based face anti-spoofing method.<n>It stimulates the capabilities of multimodal large language models to think and learn how to solve the anti-spoofing task itself.<n>It generalizes well to diverse unknown attack types in unseen target domains while providing interpretable reasoning for its authenticity decisions.
• Score: 44.37716962053431
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recently the emergence of novel presentation attacks has drawn increasing attention to face anti-spoofing. However, existing methods tend to memorize data patterns from the training set, resulting in poor generalization to unknown attack types across different scenarios and limited interpretability. To address these challenges, this paper presents a reinforcement fine-tuning-based face anti-spoofing method that stimulates the capabilities of multimodal large language models to think and learn how to solve the anti-spoofing task itself, rather than relying on the memorization of authenticity patterns. We design verifiable class consistent reward and reasoning consistent reward, and employ a GRPO-based optimization strategy to guide the model in exploring reasoning policies from multiple perspectives to maximize expected rewards. As a result, through iterative trial-and-error learning while retaining only high-reward trajectories, the model distills highly generalizable decision-making rules from the extensive solution space to effectively address cross-domain face anti-spoofing tasks. Extensive experimental results demonstrate that our method achieves state-of-the-art cross-domain generalization performance. It generalizes well to diverse unknown attack types in unseen target domains while providing interpretable reasoning for its authenticity decisions without requiring labor-intensive textual annotations for training.

Related papers

• Task-Agnostic Attacks Against Vision Foundation Models [12.487589700031661]
  It has become standard practice for machine learning practitioners to adopt publicly available pre-trained vision foundation models.<n>The study of attacks on such foundation models and their impact to multiple downstream tasks remains vastly unexplored.<n>This work proposes a general framework that forges task-agnostic adversarial examples by maximally disrupting the feature representation obtained with foundation models.
  arXiv  Detail & Related papers  (2025-03-05T19:15:14Z)
• Sustainable Self-evolution Adversarial Training [51.25767996364584]
  We propose a Sustainable Self-Evolution Adversarial Training (SSEAT) framework for adversarial training defense models.<n>We introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples.<n>We also propose an adversarial data replay module to better select more diverse and key relearning data.
  arXiv  Detail & Related papers  (2024-12-03T08:41:11Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Unlearning Backdoor Attacks through Gradient-Based Model Pruning [10.801476967873173]
  We propose a novel approach to counter backdoor attacks by treating their mitigation as an unlearning task. Our approach offers simplicity and effectiveness, rendering it well-suited for scenarios with limited data availability.
  arXiv  Detail & Related papers  (2024-05-07T00:36:56Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• Contrastive Pseudo Learning for Open-World DeepFake Attribution [67.58954345538547]
  We introduce a new benchmark called Open-World DeepFake (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios. We propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set.
  arXiv  Detail & Related papers  (2023-09-20T08:29:22Z)
• Randomized Adversarial Style Perturbations for Domain Generalization [49.888364462991234]
  We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP) The proposed algorithm perturbs the style of a feature in an adversarial direction towards a randomly selected class, and makes the model learn against being misled by the unexpected styles observed in unseen target domains. We evaluate the proposed algorithm via extensive experiments on various benchmarks and show that our approach improves domain generalization performance, especially in large-scale benchmarks.
  arXiv  Detail & Related papers  (2023-04-04T17:07:06Z)
• Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss [12.829618913069567]
  This paper presents a generalized face anti-spoofing framework that consists of three tasks: depth estimation, face parsing, and live/spoof classification. Experiments on four public datasets demonstrate that the proposed framework and training strategies are more effective than previous works for model generalization to unseen domains.
  arXiv  Detail & Related papers  (2022-11-29T06:28:00Z)
• Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries [12.312877365123267]
  Deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify. We develop a new ensemble-based solution that constructs defender models with diverse decision boundaries with respect to the original model. We present extensive experimentations using standard image classification datasets, namely MNIST, CIFAR-10 and CIFAR-100 against state-of-the-art adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-18T08:19:26Z)

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.