An Ensemble Model for Face Liveness Detection
- URL: http://arxiv.org/abs/2201.08901v1
- Date: Wed, 19 Jan 2022 12:43:39 GMT
- Title: An Ensemble Model for Face Liveness Detection
- Authors: Shashank Shekhar, Avinash Patel, Mrinal Haloi, Asif Salim
- Abstract summary: We present a passive method to detect face presentation attack using an ensemble deep learning technique.
We propose an ensemble method where multiple features of the face and background regions are learned to predict whether the user is a bonafide or an attacker.
- Score: 2.322052136673525
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we present a passive method to detect face presentation attack
a.k.a face liveness detection using an ensemble deep learning technique. Face
liveness detection is one of the key steps involved in user identity
verification of customers during the online onboarding/transaction processes.
During identity verification, an unauthenticated user tries to bypass the
verification system by several means, for example, they can capture a user
photo from social media and do an imposter attack using printouts of users
faces or using a digital photo from a mobile device and even create a more
sophisticated attack like video replay attack. We have tried to understand the
different methods of attack and created an in-house large-scale dataset
covering all the kinds of attacks to train a robust deep learning model. We
propose an ensemble method where multiple features of the face and background
regions are learned to predict whether the user is a bonafide or an attacker.
Related papers
- Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery.
We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph.
We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
arXiv Detail & Related papers (2024-05-14T10:24:19Z) - 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) - OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak.
We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks.
The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
arXiv Detail & Related papers (2022-05-24T11:29:37Z) - 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) - Introduction to Presentation Attack Detection in Face Biometrics and
Recent Advances [21.674697346594158]
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.
arXiv Detail & Related papers (2021-11-23T11:19:22Z) - 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) - Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
arXiv Detail & Related papers (2020-03-15T12:45:10Z)
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.