Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection

• URL: http://arxiv.org/abs/2111.04336v1
• Date: Mon, 8 Nov 2021 08:53:46 GMT
• Title: Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection
• Authors: Meiling Fang, Fadi Boutros, Arjan Kuijper, Naser Damer
• Abstract summary: Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-CoV-2 coronavirus. The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks. This work proposes a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance.
• Score: 5.71864964818217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-CoV-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis and MixFaceNet) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance.

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)
• Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition [47.248075664420874]
  Current general face recognition system suffers from serious performance degradation when encountering occluded scenes. This paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks. We first present a novel mask decoupling module to disentangle mask and identity information, which makes the network obtain purer identity features from visible facial components.
  arXiv  Detail & Related papers  (2023-11-20T03:23:03Z)
• 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)
• Mask-invariant Face Recognition through Template-level Knowledge Distillation [3.727773051465455]
  Masks affect the performance of previous face recognition systems. We propose a mask-invariant face recognition solution (MaskInv) In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss.
  arXiv  Detail & Related papers  (2021-12-10T16:19:28Z)
• Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models [66.07662074148142]
  We propose a physical adversarial universal perturbation (UAP) against state-of-the-art deep learning-based facial recognition models. In our experiments, we examined the transferability of our adversarial mask to a wide range of deep learning models and datasets. We validated our adversarial mask effectiveness in real-world experiments by printing the adversarial pattern on a fabric medical face mask.
  arXiv  Detail & Related papers  (2021-11-21T08:13:21Z)
• FocusFace: Multi-task Contrastive Learning for Masked Face Recognition [4.420321822469077]
  SARS-CoV-2 has presented direct and indirect challenges to the scientific community. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition.
  arXiv  Detail & Related papers  (2021-10-28T08:17:12Z)
• Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning [23.062034116854875]
  In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus is to wear a face mask. To mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. We propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network.
  arXiv  Detail & Related papers  (2021-10-01T16:44:06Z)
• MLFW: A Database for Face Recognition on Masked Faces [56.441078419992046]
  Masked LFW (MLFW) is a tool to generate masked faces from unmasked faces automatically. The recognition accuracy of SOTA models declines 5%-16% on MLFW database compared with the accuracy on the original images.
  arXiv  Detail & Related papers  (2021-09-13T09:30:10Z)
• My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition [4.171626860914305]
  We address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode.
  arXiv  Detail & Related papers  (2021-08-02T15:51:15Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)
• Real Masks and Fake Faces: On the Masked Face Presentation Attack Detection [7.324459578044212]
  Face recognition (FR) is a challenging task as several discriminative features are hidden. Face presentation attack detection (PAD) is crucial to ensure the security of FR systems. We present novel attacks with real masks placed on presentations and attacks with subjects wearing masks to reflect the current real-world situation.
  arXiv  Detail & Related papers  (2021-03-02T08:05:50Z)

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.