FaceCat: Enhancing Face Recognition Security with a Unified Generative Model Framework
- URL: http://arxiv.org/abs/2404.09193v1
- Date: Sun, 14 Apr 2024 09:01:26 GMT
- Title: FaceCat: Enhancing Face Recognition Security with a Unified Generative Model Framework
- Authors: Jiawei Chen, Xiao Yang, Yinpeng Dong, Hang Su, Jianteng Peng, Zhaoxia Yin,
- Abstract summary: Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems.
We propose FaceCat which utilizes the face generative model as a pre-trained model to improve the performance of FAS and FAD.
- Score: 30.823325635144908
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems. As a consequence of their limited practicality and generalization, some existing methods aim to devise a framework capable of concurrently detecting both threats to address the challenge. Nevertheless, these methods still encounter challenges of insufficient generalization and suboptimal robustness, potentially owing to the inherent drawback of discriminative models. Motivated by the rich structural and detailed features of face generative models, we propose FaceCat which utilizes the face generative model as a pre-trained model to improve the performance of FAS and FAD. Specifically, FaceCat elaborately designs a hierarchical fusion mechanism to capture rich face semantic features of the generative model. These features then serve as a robust foundation for a lightweight head, designed to execute FAS and FAD tasks simultaneously. As relying solely on single-modality data often leads to suboptimal performance, we further propose a novel text-guided multi-modal alignment strategy that utilizes text prompts to enrich feature representation, thereby enhancing performance. For fair evaluations, we build a comprehensive protocol with a wide range of 28 attack types to benchmark the performance. Extensive experiments validate the effectiveness of FaceCat generalizes significantly better and obtains excellent robustness against input transformations.
Related papers
- 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) - Faceptor: A Generalist Model for Face Perception [52.8066001012464]
Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture.
Layer-Attention into Faceptor enables the model to adaptively select features from optimal layers to perform the desired tasks.
Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition.
arXiv Detail & Related papers (2024-03-14T15:42:31Z) - Generalized Face Liveness Detection via De-spoofing Face Generator [58.7043386978171]
Previous Face Anti-spoofing (FAS) works face the challenge of generalizing in unseen domains.
We conduct an Anomalous cue Guided FAS (AG-FAS) method, which leverages real faces for improving model generalization via a De-spoofing Face Generator (DFG)
We then propose an Anomalous cue Guided FAS feature extraction Network (AG-Net) to further improve the FAS feature generalization via a cross-attention transformer.
arXiv Detail & Related papers (2024-01-17T06:59:32Z) - Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust.
Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model.
We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
arXiv Detail & Related papers (2023-07-31T10:22:33Z) - A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing [13.725319422213623]
Face anti-spoofing (FAS) is indispensable for a face recognition system.
We propose Geometry-Aware Interaction Network (GAIN) to distinguish between normal and abnormal movements of live and spoof presentations.
Our approach achieves state-of-the-art performance in the standard intra- and cross-dataset evaluations.
arXiv Detail & Related papers (2023-06-25T18:59:52Z) - Watch Out for the Confusing Faces: Detecting Face Swapping with the
Probability Distribution of Face Identification Models [37.49012763328351]
We propose a novel face swapping detection approach based on face identification probability distributions.
IdP_FSD is specially designed for detecting swapped faces whose identities belong to a finite set.
IdP_FSD exploits face swapping's common nature that the identity of swapped face combines that of two faces involved in swapping.
arXiv Detail & Related papers (2023-03-23T09:33:10Z) - 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) - RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely
Limited Queries [2.8532545355403123]
Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition.
In this paper, we propose automatic face warping which needs extremely limited number of queries to fool the target model.
We evaluate the robustness of proposed method in the decision-based black-box attack setting.
arXiv Detail & Related papers (2022-07-04T00:22:45Z) - FaceX-Zoo: A PyTorch Toolbox for Face Recognition [62.038018324643325]
We introduce a novel open-source framework, named FaceX-Zoo, which is oriented to the research-development community of face recognition.
FaceX-Zoo provides a training module with various supervisory heads and backbones towards state-of-the-art face recognition.
A simple yet fully functional face SDK is provided for the validation and primary application of the trained models.
arXiv Detail & Related papers (2021-01-12T11:06:50Z) - Towards Transferable Adversarial Attack against Deep Face Recognition [58.07786010689529]
Deep convolutional neural networks (DCNNs) have been found to be vulnerable to adversarial examples.
transferable adversarial examples can severely hinder the robustness of DCNNs.
We propose DFANet, a dropout-based method used in convolutional layers, which can increase the diversity of surrogate models.
We generate a new set of adversarial face pairs that can successfully attack four commercial APIs without any queries.
arXiv Detail & Related papers (2020-04-13T06:44:33Z)
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.