Makeup Presentation Attacks: Review and Detection Performance Benchmark

Rathgeb, Christian; Drozdowski, Pawel; Busch, Christoph

doi:10.1109/access.2020.3044723

Cited by 17 publications

(10 citation statements)

References 50 publications

(75 reference statements)

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“…The vulnerability of many face recognition systems has been recently assessed to facial makeup presentation in [32]. Furthermore, the same research team presented a facial retouch detection method based on analysis of photo response non-uniformity (PRNU) in [33] and conducted a review and benchmarking for makeup presentation attacks methods in [34].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Models for Automatic Makeup Detection

Alzahrani

Al-Bander

Al-Nuaimy

2021

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Makeup can disguise facial features, which results in degradation in the performance of many facial-related analysis systems, including face recognition, facial landmark characterisation, aesthetic quantification and automated age estimation methods. Thus, facial makeup is likely to directly affect several real-life applications such as cosmetology and virtual cosmetics recommendation systems, security and access control, and social interaction. In this work, we conduct a comparative study and design automated facial makeup detection systems leveraging multiple learning schemes from a single unconstrained photograph. We have investigated and studied the efficacy of deep learning models for makeup detection incorporating the use of transfer learning strategy with semi-supervised learning using labelled and unlabelled data. First, during the supervised learning, the VGG16 convolution neural network, pre-trained on a large dataset, is fine-tuned on makeup labelled data. Secondly, two unsupervised learning methods, which are self-learning and convolutional auto-encoder, are trained on unlabelled data and then incorporated with supervised learning during semi-supervised learning. Comprehensive experiments and comparative analysis have been conducted on 2479 labelled images and 446 unlabelled images collected from six challenging makeup datasets. The obtained results reveal that the convolutional auto-encoder merged with supervised learning gives the best makeup detection performance achieving an accuracy of 88.33% and area under ROC curve of 95.15%. The promising results obtained from conducted experiments reveal and reflect the efficiency of combining different learning strategies by harnessing labelled and unlabelled data. It would also be advantageous to the beauty industry to develop such computational intelligence methods.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Models for Automatic Makeup Detection

Alzahrani

Al-Bander

Al-Nuaimy

2021

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“…In physical face attacks, a.k.a. Presentation Attacks (PAs), real subjects are often impersonated using artefacts such as photographs, videos, makeup, and masks [13,29,38,39]. Face recognition systems are known to be vulnerable against these attacks unless proper detection methods are implemented [14,19].…”

Section: Introductionmentioning

confidence: 99%

DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame

Hernandez-Ortega

Tolosana

Fiérrez

et al. 2022

Advances in Computer Vision and Pattern Recognition

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This chapter describes a DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. This chapter explores to what extent rPPG is useful for the detection of DeepFake videos. We analyze the recent fake detector named DeepFakesON-Phys that is based on a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. DeepFakesON-Phys has been experimentally evaluated using the latest public databases in the field: Celeb-DF v2 and DFDC. The results achieved for DeepFake detection based on a single frame are over 98% AUC (Area Under the Curve) on both databases, proving the success of fake detectors based on physiological measurement to detect the latest DeepFake videos. In this chapter, we also propose and study heuristical and statistical approaches for performing continuous DeepFake detection by combining scores from consecutive frames with low latency and high accuracy (100% on the Celeb-DF v2 evaluation dataset). We show that combining scores extracted from short-time video sequences can improve the discrimination power of DeepFakesON-Phys.

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“…A presentation attack aims to subvert the face recognition system by presenting a facial biometric artifact, including a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks [7]. It has recently been demonstrated that makeup can also be abused to launch presentation attacks [8].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks Against Face Recognition: A Comprehensive Study

2021

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Face recognition (FR) systems have demonstrated reliable verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC). In an advanced FR system with deep learning-based architecture, however, promoting the recognition efficiency alone is not sufficient, and the system should also withstand potential kinds of attacks. Recent studies show that (deep) FR systems exhibit an intriguing vulnerability to imperceptible or perceptible but natural-looking adversarial input images that drive the model to incorrect output predictions. In this article, we present a comprehensive survey on adversarial attacks against FR systems and elaborate on the competence of new countermeasures against them. Further, we propose a taxonomy of existing attack and defense methods based on different criteria. We compare attack methods on the orientation, evaluation process, and attributes, and defense approaches on the category. Finally, we discuss the challenges and potential research direction.

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Makeup Presentation Attacks: Review and Detection Performance Benchmark

Cited by 17 publications

References 50 publications

Deep Learning Models for Automatic Makeup Detection

Deep Learning Models for Automatic Makeup Detection

DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame

Adversarial Attacks Against Face Recognition: A Comprehensive Study

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