Deep Anomaly Detection for Generalized Face Anti-Spoofing

Pérez‐Cabo, Daniel; Jiménez‐Cabello, David; Costa‐Pazo, Artur; López-Sastre, Roberto J.

doi:10.1109/cvprw.2019.00201

Cited by 52 publications

(36 citation statements)

References 29 publications

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“…Accordingly, the face PAD problem may be better characterised as an open-set recognition task in a real-world setting, necessitating a different approach to be dealt with. The importance of unseen attacks has not only been identified in the context of face PAD [1]- [6] but also in other biometric modalities [7]- [11], motivating lots of intensive research on the problem. Among others options, one potential approach to the problem is that of one-class classification (OCC) [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Matrix-Regularized One-Class Multiple Kernel Learning for Unseen Face Presentation Attack Detection

Arashloo

2021

IEEE Trans.Inform.Forensic Secur.

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Section: Introductionmentioning

confidence: 99%

Matrix-Regularized One-Class Multiple Kernel Learning for Unseen Face Presentation Attack Detection

Arashloo

2021

IEEE Trans.Inform.Forensic Secur.

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“…The few-shot learning was utilized to identify similar regions and extract more discriminative features. Perez-Cabo et al [16] proposed a deep metric learning method for the generalized presentation attack detection problem, in which a triplet focal loss was defined to regularize a new "metric-softmax" loss. They used the few-shot learning to improve the feature representation and distinguish attacks only using the image data.…”

Section: B Few-shot Learning In Industrial Applicationsmentioning

confidence: 99%

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Zhou

Liang

Shimizu

et al. 2021

IEEE Trans. Ind. Inf.

206

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With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial Cyber-Physical Systems (CPS). Intelligent anomaly detection for identifying cyber/physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this study, we propose a Few-Shot Learning model with Siamese Convolution Neural Network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve False Alarm Rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

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“…Current state-of-the-art approaches have achieved unprecedented results in standard benchmarks and datasets [2,3]. The reasons are clear: (a) the available data is orders of magnitude under the amount of the model's parameters and (b) all the presentation attacks are known beforehand and are completely represented by the training data.…”

Section: Introductionmentioning

confidence: 99%

Face presentation attack detection. A comprehensive evaluation of the generalisation problem

et al. 2021

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Face recognition technology is now mature enough to reach commercial products, such as smart phones or tablets. However, it still needs to increase robustness against imposter attacks. In this regard, face Presentation Attack Detection (face-PAD) is a key component in providing trustable facial access to digital devices. Despite the success of several face-PAD works in publicly available datasets, most of them fail to reach the market, revealing the lack of evaluation frameworks that represent realistic settings. Here, an extensive analysis of the generalisation problem in face-PAD is provided, jointly with an evaluation strategy based on the aggregation of most publicly available datasets and a set of novel protocols to cover the most realistic settings, including a novel demographic bias analysis. Besides, a new finegrained categorisation of presentation attacks and instruments is provided, enabling higher flexibility in assessing the generalisation of different algorithms under a common framework. As a result, GRAD-GPAD v2, a comprehensive and modular framework is presented to evaluate the performance of face-PAD approaches in realistic settings, enabling accountability and fair comparison of most face-PAD approaches in the literature.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Deep Anomaly Detection for Generalized Face Anti-Spoofing

Cited by 52 publications

References 29 publications

Matrix-Regularized One-Class Multiple Kernel Learning for Unseen Face Presentation Attack Detection

Matrix-Regularized One-Class Multiple Kernel Learning for Unseen Face Presentation Attack Detection

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Face presentation attack detection. A comprehensive evaluation of the generalisation problem

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