FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning

• URL: http://arxiv.org/abs/2412.12032v2
• Date: Sat, 28 Dec 2024 13:48:32 GMT
• Title: FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning
• Authors: Gaojian Wang, Feng Lin, Tong Wu, Zhenguang Liu, Zhongjie Ba, Kui Ren,
• Abstract summary: We propose a self-supervised pretraining framework to learn fundamental representations of real face images. Our model transfers better than supervised pretraining, visual and facial self-supervised learning arts, and even outperforms task-specialized SOTA methods.
• Score: 27.34249750803211
• License:
• Abstract: This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable facial representation that boosts various face security tasks with respect to generalization performance? We make the first attempt and propose a self-supervised pretraining framework to learn fundamental representations of real face images, FSFM, that leverages the synergy between masked image modeling (MIM) and instance discrimination (ID). We explore various facial masking strategies for MIM and present a simple yet powerful CRFR-P masking, which explicitly forces the model to capture meaningful intra-region consistency and challenging inter-region coherency. Furthermore, we devise the ID network that naturally couples with MIM to establish underlying local-to-global correspondence via tailored self-distillation. These three learning objectives, namely 3C, empower encoding both local features and global semantics of real faces. After pretraining, a vanilla ViT serves as a universal vision foundation model for downstream face security tasks: cross-dataset deepfake detection, cross-domain face anti-spoofing, and unseen diffusion facial forgery detection. Extensive experiments on 10 public datasets demonstrate that our model transfers better than supervised pretraining, visual and facial self-supervised learning arts, and even outperforms task-specialized SOTA methods.

Related papers

• ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition [60.15830516741776]
  Synthetic face recognition (SFR) aims to generate datasets that mimic the distribution of real face data. We introduce a diffusion-fueled SFR model termed $textID3$. $textID3$ employs an ID-preserving loss to generate diverse yet identity-consistent facial appearances.
  arXiv  Detail & Related papers  (2024-09-26T06:46:40Z)
• Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition [1.4374467687356276]
  This paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification. We suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset.
  arXiv  Detail & Related papers  (2024-03-19T16:21:47Z)
• Self-Supervised Facial Representation Learning with Facial Region Awareness [13.06996608324306]
  Self-supervised pre-training has been proven to be effective in learning transferable representations that benefit various visual tasks. Recent efforts toward this goal are limited to treating each face image as a whole. We propose a novel self-supervised facial representation learning framework to learn consistent global and local facial representations.
  arXiv  Detail & Related papers  (2024-03-04T15:48:56Z)
• 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. We propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces. 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)
• A Generalist FaceX via Learning Unified Facial Representation [77.74407008931486]
  FaceX is a novel facial generalist model capable of handling diverse facial tasks simultaneously. Our versatile FaceX achieves competitive performance compared to elaborate task-specific models on popular facial editing tasks.
  arXiv  Detail & Related papers  (2023-12-31T17:41:48Z)
• A Generative Framework for Self-Supervised Facial Representation Learning [18.094262972295702]
  Self-supervised representation learning has gained increasing attention for strong generalization ability without relying on paired datasets. Self-supervised facial representation learning remains unsolved due to the coupling of facial identities, expressions, and external factors like pose and light. We propose LatentFace, a novel generative framework for self-supervised facial representations.
  arXiv  Detail & Related papers  (2023-09-15T09:34:05Z)
• Toward High Quality Facial Representation Learning [58.873356953627614]
  We propose a self-supervised pre-training framework, called Mask Contrastive Face (MCF) We use feature map of a pre-trained visual backbone as a supervision item and use a partially pre-trained decoder for mask image modeling. Our model achieves 0.932 NME_diag$ for AFLW-19 face alignment and 93.96 F1 score for LaPa face parsing.
  arXiv  Detail & Related papers  (2023-09-07T09:11:49Z)
• DiffFace: Diffusion-based Face Swapping with Facial Guidance [24.50570533781642]
  We propose a diffusion-based face swapping framework for the first time, called DiffFace. It is composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability.
  arXiv  Detail & Related papers  (2022-12-27T02:51:46Z)
• FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping [62.38898610210771]
  We present a new single-stage method for subject face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR)
  arXiv  Detail & Related papers  (2022-10-19T11:31:38Z)
• DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition [94.96686189033869]
  We propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) DotFAN can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets collected from other domains. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity.
  arXiv  Detail & Related papers  (2020-02-23T08:16:34Z)

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.