Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels

• URL: http://arxiv.org/abs/2204.10675v1
• Date: Fri, 22 Apr 2022 12:43:25 GMT
• Title: Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels
• Authors: Shervin Rahimzadeh Arashloo
• Abstract summary: The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown types of attack. We formulate a convex localised multiple kernel learning algorithm by imposing a joint matrix-norm constraint on the collection of local kernel weights.
• Score: 15.000818334408802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown types of attack. While earlier studies have revealed the benefits of ensemble methods, and in particular, a multiple kernel learning approach to the problem, one limitation of such techniques is that they typically treat the entire observation space similarly and ignore any variability and local structure inherent to the data. This work studies this aspect of the face presentation attack detection problem in relation to multiple kernel learning in a one-class setting to benefit from intrinsic local structure in bona fide face samples. More concretely, inspired by the success of the one-class Fisher null formalism, we formulate a convex localised multiple kernel learning algorithm by imposing a joint matrix-norm constraint on the collection of local kernel weights and infer locally adaptive weights for zero-shot one-class unseen attack detection. We present a theoretical study of the proposed localised MKL algorithm using Rademacher complexities to characterise its generalisation capability and demonstrate the advantages of the proposed technique over some other options. An assessment of the proposed approach on general object image datasets illustrates its efficacy for abnormality and novelty detection while the results of the experiments on face PAD datasets verifies its potential in detecting unknown/unseen face presentation attacks.

Related papers

• Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies [7.472760645164855]
  Weak or rare signals can remain hidden within the apparent regularity of normal data, creating a gap in our ability to detect and interpret anomalies.<n>We propose a class of self-organizing local kernels that adaptively partition the representation space around regions of statistical imbalance.<n>We provide theoretical insights into the mechanisms that drive detection and self-organization in the proposed model, and demonstrate the effectiveness of this approach on realistic high-dimensional problems.
  arXiv  Detail & Related papers  (2025-11-05T00:55:56Z)
• Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks [47.18575262588692]
  We propose a novel Multi-Space Prototypical Learning framework tailored for few-shot attack detection. By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks. Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types.
  arXiv  Detail & Related papers  (2024-12-28T00:09:46Z)
• A Comprehensive Augmentation Framework for Anomaly Detection [1.6114012813668932]
  This paper analyzes crucial traits of simulated anomalies that contribute to the training of reconstructive networks. We integrate this framework with a reconstruction-based approach and concurrently propose a split training strategy.
  arXiv  Detail & Related papers  (2023-08-29T07:00:35Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Dual Contrastive Learning for General Face Forgery Detection [64.41970626226221]
  We propose a novel face forgery detection framework, named Dual Contrastive Learning (DCL), which constructs positive and negative paired data. To explore the essential discrepancies, Intra-Instance Contrastive Learning (Intra-ICL) is introduced to focus on the local content inconsistencies prevalent in the forged faces.
  arXiv  Detail & Related papers  (2021-12-27T05:44:40Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Deep Random Projection Outlyingness for Unsupervised Anomaly Detection [1.2249546377051437]
  The original random projection outlyingness measure is modified and associated with a neural network to obtain an unsupervised anomaly detection method. The performance of the proposed neural network approach is comparable to a state-of-the-art anomaly detection method.
  arXiv  Detail & Related papers  (2021-06-08T14:13:43Z)
• A Compact Deep Learning Model for Face Spoofing Detection [4.250231861415827]
  presentation attack detection (PAD) has received significant attention from research communities. We address the problem via fusing both wide and deep features in a unified neural architecture. The procedure is done on different spoofing datasets such as ROSE-Youtu, SiW and NUAA Imposter.
  arXiv  Detail & Related papers  (2021-01-12T21:20:09Z)
• Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection [50.03939695025513]
  We propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature. In this paper, we consider the non-robust features as a common property of adversarial examples, and we deduce it is possible to find a cluster in representation space corresponding to the property. This idea leads us to probability estimate distribution of adversarial representations in a separate cluster, and leverage the distribution for a likelihood based adversarial detector.
  arXiv  Detail & Related papers  (2020-12-07T07:21:18Z)
• 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)
• Open Set Recognition with Conditional Probabilistic Generative Models [51.40872765917125]
  We propose Conditional Probabilistic Generative Models (CPGM) for open set recognition. CPGM can detect unknown samples but also classify known classes by forcing different latent features to approximate conditional Gaussian distributions. Experiment results on multiple benchmark datasets reveal that the proposed method significantly outperforms the baselines.
  arXiv  Detail & Related papers  (2020-08-12T06:23:49Z)
• Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression [15.000818334408802]
  The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario. A pure one-class face presentation attack detection approach based on kernel regression is developed.
  arXiv  Detail & Related papers  (2019-12-31T11:53:20Z)

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.