Presentation Attack detection using Wavelet Transform and Deep Residual Neural Net

• URL: http://arxiv.org/abs/2312.00040v1
• Date: Thu, 23 Nov 2023 20:21:49 GMT
• Title: Presentation Attack detection using Wavelet Transform and Deep Residual Neural Net
• Authors: Prosenjit Chatterjee, Alex Yalchin, Joseph Shelton, Kaushik Roy, Xiaohong Yuan, and Kossi D. Edoh
• Abstract summary: Biometric substances can be deceived by the imposters in several ways. The bio-metric images, especially the iris and face, are vulnerable to different presentation attacks. This research applies deep learning approaches to mitigate presentation attacks in a biometric access control system.
• Score: 5.425986555749844
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Biometric authentication is becoming more prevalent for secured authentication systems. However, the biometric substances can be deceived by the imposters in several ways. Among other imposter attacks, print attacks, mask attacks, and replay attacks fall under the presentation attack category. The bio-metric images, especially the iris and face, are vulnerable to different presentation attacks. This research applies deep learning approaches to mitigate presentation attacks in a biometric access control system. Our contribution in this paper is two-fold: First, we applied the wavelet transform to extract the features from the biometric images. Second, we modified the deep residual neural net and applied it to the spoof datasets in an attempt to detect the presentation attacks. This research applied the proposed approach to biometric spoof datasets, namely ATVS, CASIA two class, and CASIA cropped image sets. The datasets used in this research contain images that are captured in both a controlled and uncontrolled environment along with different resolutions and sizes. We obtained the best accuracy of 93% on the ATVS Iris datasets. For CASIA two class and CASIA cropped datasets, we achieved test accuracies of 91% and 82%, respectively.

Related papers

• A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems [0.0]
  We propose a universal presentation attack detection method for contactless fingerprints. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions.
  arXiv  Detail & Related papers  (2023-10-23T15:46:47Z)
• An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network [3.5558308387389626]
  Presentation Attack (PA) or spoofing is one of the threats caused by presenting a spoof of a genuine fingerprint to the sensor of Automatic Fingerprint Recognition Systems (AFRS) This paper proposes a CNN based technique that uses a Generative Adversarial Network (GAN) to augment the dataset with spoof samples generated from the proposed Open Patch Generator (OPG) An overall accuracy of 96.20%, 94.97%, and 92.90% has been achieved on the LivDet 2015, 2017, and 2019 databases, respectively under the LivDet protocol scenarios.
  arXiv  Detail & Related papers  (2023-06-06T10:52:06Z)
• An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detection [1.2599533416395763]
  We present a novel visual explanation approach named Ensemble XAI to provide a more comprehensive visual explanation for a deep learning prognostic model (EfficientNet-Grad1) The experiments have been performed on three publicly available datasets namely Face Research Lab London Set, Wide Multi-Channel Presentation Attack (WMCA) and Makeup Induced Face Spoofing (MIFS)
  arXiv  Detail & Related papers  (2023-04-23T13:43:06Z)
• Mitigating Presentation Attack using DCGAN and Deep CNN [0.0]
  This research aims at identifying the areas where presentation attack can be prevented. Our work focusses on generating photorealistic synthetic images from the real image sets. We applied the deep neural net techniques on three different biometric image datasets.
  arXiv  Detail & Related papers  (2022-06-22T19:40:08Z)
• Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning [54.15303628138665]
  Face anti-spoofing (FAS) plays a vital role in preventing face recognition systems from presentation attacks. Existing face anti-spoofing datasets lack diversity due to the insufficient identity and insignificant variance. We propose Dual Spoof Disentanglement Generation framework to tackle this challenge by "anti-spoofing via generation"
  arXiv  Detail & Related papers  (2021-12-01T15:36:59Z)
• Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation [100.69458267888962]
  Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. Due to legal and privacy issues, training data (real face images and spoof images) are not allowed to be directly shared between different data sources. We propose a Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation framework.
  arXiv  Detail & Related papers  (2021-10-25T02:51:05Z)
• Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System [103.5604680001633]
  Anti-spoofing against high-resolution rendering replay of paper photos or digital videos remains an open problem. We propose a simple yet effective face anti-spoofing system, termed Aurora Guard (AG)
  arXiv  Detail & Related papers  (2021-02-01T09:17:18Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)
• Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study [0.5801044612920815]
  Cross-dataset evaluation on face PAD showed better generalization than state of the art. We propose the use of a single deep network trained to detect both face and iris attacks.
  arXiv  Detail & Related papers  (2020-04-25T02:06:19Z)
• Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario [50.36920272392624]
  Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. We present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face.
  arXiv  Detail & Related papers  (2020-03-18T03:04:03Z)

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.