Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances

• URL: http://arxiv.org/abs/2111.11794v1
• Date: Tue, 23 Nov 2021 11:19:22 GMT
• Title: Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances
• Authors: Javier Hernandez-Ortega, Julian Fierrez, Aythami Morales and Javier Galbally
• Abstract summary: The next pages present the different presentation attacks that a face recognition system can confront. We make an introduction of the current status of face recognition, its level of deployment, and its challenges. We review different types of presentation attack methods, from simpler to more complex ones, and in which cases they could be effective.
• Score: 21.674697346594158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The main scope of this chapter is to serve as an introduction to face presentation attack detection, including key resources and advances in the field in the last few years. The next pages present the different presentation attacks that a face recognition system can confront, in which an attacker presents to the sensor, mainly a camera, a Presentation Attack Instrument (PAI), that is generally a photograph, a video, or a mask, to try to impersonate a genuine user. First, we make an introduction of the current status of face recognition, its level of deployment, and its challenges. In addition, we present the vulnerabilities and the possible attacks that a face recognition system may be exposed to, showing that way the high importance of presentation attack detection methods. We review different types of presentation attack methods, from simpler to more complex ones, and in which cases they could be effective. Then, we summarize the most popular presentation attack detection methods to deal with these attacks. Finally, we introduce public datasets used by the research community for exploring vulnerabilities of face biometrics to presentation attacks and developing effective countermeasures against known PAIs.

Related papers

• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• Principles of Designing Robust Remote Face Anti-Spoofing Systems [60.05766968805833]
  This paper sheds light on the vulnerabilities of state-of-the-art face anti-spoofing methods against digital attacks. It presents a comprehensive taxonomy of common threats encountered in face anti-spoofing systems.
  arXiv  Detail & Related papers  (2024-06-06T02:05:35Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection [0.0]
  We study state-of-the-art to cover the challenges and solutions related to presentation attack detection. A presentation attack is an attempt to present a non-live face, such as a photo, video, mask, and makeup, to the camera. We introduce an efficient active presentation attack detection approach that overcomes weaknesses in the existing literature.
  arXiv  Detail & Related papers  (2022-12-14T00:30:09Z)
• Face Presentation Attack Detection [59.05779913403134]
  Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
  arXiv  Detail & Related papers  (2022-12-07T14:51:17Z)
• Attacking Face Recognition with T-shirts: Database, Vulnerability Assessment and Detection [0.0]
  We propose a new T-shirt Face Presentation Attack database of 1,608 T-shirt attacks using 100 unique presentation attack instruments. We show that this type of attack can compromise the security of face recognition systems and that some state-of-the-art attack detection mechanisms fail to robustly generalize to the new attacks.
  arXiv  Detail & Related papers  (2022-11-14T14:11:23Z)
• Digital and Physical Face Attacks: Reviewing and One Step Further [31.780516471483985]
  Face presentation attacks (FPA) have raised pressing mistrust concerns. Besides physical face attacks, face videos/images are vulnerable to a wide variety of digital attack techniques launched by malicious hackers. This survey aims to build the integrity of face forensics by providing thorough analyses of existing literature and highlighting the issues requiring further attention.
  arXiv  Detail & Related papers  (2022-09-29T11:25:52Z)
• Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances [13.393219486444604]
  Presentation attacks stand out as some of the most relevant and studied. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks.
  arXiv  Detail & Related papers  (2021-11-24T12:43:06Z)
• Differential Anomaly Detection for Facial Images [15.54185745912878]
  Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation. Most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time. We introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images.
  arXiv  Detail & Related papers  (2021-10-07T13:45:13Z)
• Robust Physical-World Attacks on Face Recognition [52.403564953848544]
  Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) Recent studies have shown that DNNs are very vulnerable to adversarial examples, raising serious concerns on the security of real-world face recognition. We study sticker-based physical attacks on face recognition for better understanding its adversarial robustness.
  arXiv  Detail & Related papers  (2021-09-20T06:49:52Z)
• 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)

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.