Готові списки джерел за темами / Face presentation attack detection

Добірка наукової літератури з теми "Face presentation attack detection"

Автор: Grafiati
Опубліковано: 23 вересня 2023

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Статті в журналах з теми "Face presentation attack detection"

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Abdullakutty, Faseela, Pamela Johnston, and Eyad Elyan. "Fusion Methods for Face Presentation Attack Detection." Sensors 22, no. 14 (July 12, 2022): 5196. http://dx.doi.org/10.3390/s22145196.

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Анотація:
Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. In this paper, we present a simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features. Extensive experiments clearly show the benefit of enriching the feature space to improve detection rates by using three common public datasets, namely CASIA, Replay Attack, and SiW. This work opens future research to improve face presentation attack detection by exploring new characterizing features and fusion strategies.
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Zhu, Shuaishuai, Xiaobo Lv, Xiaohua Feng, Jie Lin, Peng Jin, and Liang Gao. "Plenoptic Face Presentation Attack Detection." IEEE Access 8 (2020): 59007–14. http://dx.doi.org/10.1109/access.2020.2980755.

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Kowalski, Marcin. "A Study on Presentation Attack Detection in Thermal Infrared." Sensors 20, no. 14 (July 17, 2020): 3988. http://dx.doi.org/10.3390/s20143988.

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Анотація:
Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.
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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Multi-Modal Face Presentation Attack Detection." Synthesis Lectures on Computer Vision 9, no. 1 (July 27, 2020): 1–88. http://dx.doi.org/10.2200/s01032ed1v01y202007cov017.

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Alshareef, Norah, Xiaohong Yuan, Kaushik Roy, and Mustafa Atay. "A Study of Gender Bias in Face Presentation Attack and Its Mitigation." Future Internet 13, no. 9 (September 14, 2021): 234. http://dx.doi.org/10.3390/fi13090234.

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Анотація:
In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models’ performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model’s performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.
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Benlamoudi, Azeddine, Salah Eddine Bekhouche, Maarouf Korichi, Khaled Bensid, Abdeldjalil Ouahabi, Abdenour Hadid, and Abdelmalik Taleb-Ahmed. "Face Presentation Attack Detection Using Deep Background Subtraction." Sensors 22, no. 10 (May 15, 2022): 3760. http://dx.doi.org/10.3390/s22103760.

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Анотація:
Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vote (MV) algorithm, which uses different face presentation attack instruments (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the face anti-spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results are very interesting and are much better than those obtained by state-of-the-art methods. For instance, on the REPLAY-ATTACK database, we were able to attain a half-total error rate (HTER) of 0.62% and an equal error rate (EER) of 0.58%. We attained an EER of 0% on both the CASIA-FASD and the MSU MFSD databases.
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Wan, Jun, Sergio Escalera, Hugo Jair Escalante, Guodong Guo, and Stan Z. Li. "Special Issue on Face Presentation Attack Detection." IEEE Transactions on Biometrics, Behavior, and Identity Science 3, no. 3 (July 2021): 282–84. http://dx.doi.org/10.1109/tbiom.2021.3089903.

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Nguyen, Dat, Tuyen Pham, Min Lee, and Kang Park. "Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information." Sensors 19, no. 2 (January 20, 2019): 410. http://dx.doi.org/10.3390/s19020410.

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Анотація:
Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.
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Ramachandra, Raghavendra, and Christoph Busch. "Presentation Attack Detection Methods for Face Recognition Systems." ACM Computing Surveys 50, no. 1 (April 13, 2017): 1–37. http://dx.doi.org/10.1145/3038924.

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Peng, Fei, Le Qin, and Min Long. "Face presentation attack detection using guided scale texture." Multimedia Tools and Applications 77, no. 7 (May 13, 2017): 8883–909. http://dx.doi.org/10.1007/s11042-017-4780-0.

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Дисертації з теми "Face presentation attack detection"

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Boulkenafet, Z. (Zinelabidine). "Face presentation attack detection using texture analysis." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526219257.

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Анотація:
Abstract In the last decades, face recognition systems have evolved a lot in terms of performance. As a result, this technology is now considered as mature and is applied in many real world applications from border control to financial transactions and computer security. Yet, many studies show that these systems suffer from vulnerabilities to spoofing attacks, a weakness that may limit their usage in many cases. A face spoofing attack or presentation attack occurs when someone tries to masquerade as someone else by presenting a fake face in front of the face recognition camera. To protect the recognition systems against attacks of this kind, many face anti-spoofing methods have been proposed. These methods have shown good performances on the existing face anti-spoofing databases. However, their performances degrade drastically under real world variations (e.g., illumination and camera device variations). In this thesis, we concentrate on improving the generalization capabilities of the face anti-spoofing methods with a particular focus on the texture based techniques. In contrast to most existing texture based methods aiming at extracting texture features from gray-scale images, we propose a joint color-texture analysis. First, the face images are converted into different color spaces. Then, the feature histograms computed over each image band are concatenated and used for discriminating between real and fake face images. Our experiments conducted on three color spaces: RGB, HSV and YCbCr show that extracting the texture information from separated luminance chrominance color spaces (HSV and YCbCr) yields to better performances compared to gray-scale and RGB image representations. Moreover, to deal with the problem of illumination and image-resolution variations, we propose to extract this texture information from different scale images. In addition to representing the face images in different scales, the multi-scale filtering methods also act as pre-processing against factors such as noise and illumination. Although our obtained results are better than the state of the art, they are still far from the requirements of real world applications. Thus, to help in the development of robust face anti-spoofing methods, we collected a new challenging face anti-spoofing database using six camera devices in three different illumination and environmental conditions. Furthermore, we have organized a competition on the collected database where fourteen face anti-spoofing methods have been assessed and compared
Tiivistelmä Kasvontunnistusjärjestelmien suorituskyky on parantunut huomattavasti viime vuosina. Tästä syystä tätä teknologiaa pidetään nykyisin riittävän kypsänä ja käytetään jo useissa käytännön sovelluksissa kuten rajatarkastuksissa, rahansiirroissa ja tietoturvasovelluksissa. Monissa tutkimuksissa on kuitenkin havaittu, että nämä järjestelmät ovat myös haavoittuvia huijausyrityksille, joissa joku yrittää esiintyä jonakin toisena henkilönä esittämällä kameralle jäljennöksen kohdehenkilön kasvoista. Tämä haavoittuvuus rajoittaa kasvontunnistuksen laajempaa käyttöä monissa sovelluksissa. Tunnistusjärjestelmien turvaamiseksi on kehitetty lukuisia menetelmiä tällaisten hyökkäysten torjumiseksi. Nämä menetelmät ovat toimineet hyvin tätä tarkoitusta varten kehitetyillä kasvotietokannoilla, mutta niiden suorituskyky huononee dramaattisesti todellisissa käytännön olosuhteissa, esim. valaistuksen ja käytetyn kuvantamistekniikan variaatioista johtuen. Tässä työssä yritämme parantaa kasvontunnistuksen huijauksen estomenetelmien yleistämiskykyä keskittyen erityisesti tekstuuripohjaisiin menetelmiin. Toisin kuin useimmat olemassa olevat tekstuuripohjaiset menetelmät, joissa tekstuuripiirteitä irrotetaan harmaasävykuvista, ehdotamme väritekstuurianalyysiin pohjautuvaa ratkaisua. Ensin kasvokuvat muutetaan erilaisiin väriavaruuksiin. Sen jälkeen kuvan jokaiselta kanavalta erikseen lasketut piirrehistogrammit yhdistetään ja käytetään erottamaan aidot ja väärät kasvokuvat toisistaan. Kolmeen eri väriavaruuteen, RGB, HSV ja YCbCr, perustuvat testimme osoittavat, että tekstuuri-informaation irrottaminen HSV- ja YCbCr-väriavaruuksien erillisistä luminanssi- ja krominanssikuvista parantaa suorituskykyä kuvien harmaasävy- ja RGB-esitystapoihin verrattuna. Valaistuksen ja kuvaresoluution variaation takia ehdotamme myös tämän tekstuuri-informaation irrottamista eri tavoin skaalatuista kuvista. Sen lisäksi, että itse kasvot esitetään eri skaaloissa, useaan skaalaan perustuvat suodatusmenetelmät toimivat myös esikäsittelynä sellaisia suorituskykyä heikentäviä tekijöitä vastaan kuten kohina ja valaistus. Vaikka tässä tutkimuksessa saavutetut tulokset ovat parempia kuin uusinta tekniikkaa edustavat tulokset, ne ovat kuitenkin vielä riittämättömiä reaalimaailman sovelluksissa tarvittavaan suorituskykyyn. Sen takia edistääksemme uusien robustien kasvontunnistuksen huijaamisen ilmaisumenetelmien kehittämistä kokosimme uuden, haasteellisen huijauksenestotietokannan käyttäen kuutta kameraa kolmessa erilaisessa valaistus- ja ympäristöolosuhteessa. Järjestimme keräämällämme tietokannalla myös kansainvälisen kilpailun, jossa arvioitiin ja verrattiin neljäätoista kasvontunnistuksen huijaamisen ilmaisumenetelmää
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DOMECH, GUILLERMO ESTRADA. "AN ASSESSMENT OF PRESENTATION ATTACK DETECTION METHODS FOR FACE RECOGNITION SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35526@1.

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Анотація:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
As vulnerabilidades dos Sistemas de Reconhecimento Facial (FRS) aos Ataques de Apresentação (PA) foram recentemente reconhecidas pela comunidade biométrica, mas ainda existe a falta de técnicas faciais de Detecção de Ataque de Apresentação (PAD) baseadas em software que apresentam desempenho robusto em cenários de autenticação realistas. O objetivo principal desta dissertação é analisar, avaliar e comparar alguns dos métodos baseados em atributos do estado-da-arte para PAD facial em uma variedade de condições, considerando três dos bancos de dados de fraude facial publicamente disponíveis 3DMAD, REPLAY-MOBILE e OULU-NPU. No presente trabalho, os métodos de PAD baseados em descritores de texturas LBP-RGB, BSIF-RGB e IQM foram investigados. Ademais, um Autoencoder Convolucional (CAE), um descritor de atributos aprendidos, também foi implementado e avaliado. Também, abordagens de classificação de uma e duas classes foram implementadas e avaliadas. Os experimentos realizados neste trabalho foram concebidos para medir o desempenho de diferentes esquemas de PAD em duas condições: (i) intra-banco de dados e (ii) inter-banco de dados. Os resultados revelaram que a eficácia dos atributos aprendidos pelo CAE em esquemas de PAD baseados na abordagem de classificação de duas classes fornece, em geral, o melhor desempenho em protocolos de avaliação intra-banco de dados. Os resultados também indicam que os esquemas de PAD baseados na abordagem de classificação de uma classe não são inferiores em comparação às suas contrapartes de duas classes nas avaliações inter-banco de dados.
The vulnerabilities of Face Recognition Systems (FRS) to Presentation Attacks (PA) have been recently recognized by the biometric community, but there is still a lack of generalized software-based facial Presentation Attack Detection (PAD) techniques that perform robustly in realistic authentication scenarios. The main objective of this dissertation is to analyze, evaluate and compare some of the most relevant, state-of-the-art feature-based methods for facial PAD in a variety of conditions, considering three of the facial spoofing databases publicly available 3DMAD, REPLAYMOBILE and OULU-NPU. In the current work, PAD methods based on LBP-RGB, BSIF-RGB and IQM hand-crafted texture descriptors were investigated. Additionally, a Convolutional Autoencoder (CAE), a learned feature descriptor, was also implemented and evaluated. Furthermore, oneclass and two-class classification approaches were implemented and evaluated. The experiments conducted in this work were designed to measure the performance of different PAD schemes in two conditions, namely: (i) intradatabase and (ii) inter-database (or cross-database). The results revealed the effectiveness of the features learned by CAE in two-class classification PAD schemes provide, in general, the best performance in intra-database evaluation protocols. The results also indicate that PAD schemes based on one-class classification approach are not inferior as compared to their twoclass counterpart in the inter-database evaluations.
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3

Öberg, Fredrik. "Investigation on how presentation attack detection can be used to increase security for face recognition as biometric identification : Improvements on traditional locking system." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42294.

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Анотація:
Biometric identification has already been applied to society today, as today’s mobile phones use fingerprints and other methods like iris and the face itself. With growth for technologies like computer vision, the Internet of Things, Artificial Intelligence, The use of face recognition as a biometric identification on ordinary doors has become increasingly common. This thesis studies is looking into the possibility of replacing regular door locks with face recognition or supplement the locks to increase security by using a pre-trained state-of-the-art face recognition method based on a convolution neural network. A subsequent investigation concluded that a networks based face recognition are is highly vulnerable to attacks in the form of presentation attacks. This study investigates protection mechanisms against these forms of attack by developing a presentation attack detection and analyzing its performance. The obtained results from the proof of concept  showed that local binary patterns histograms as a presentation attack detection could help the state of art face recognition to avoid attacks up to 88\% of the attacks the convolution neural network approved without the presentation attack detection. However, to replace traditional locks, more work must be done to detect more attacks in form of both higher percentage of attacks blocked by the system and the types of attack that can be done. Nevertheless, as a supplement face recognition represents a promising technology to supplement traditional door locks, enchaining their security by complementing the authorization with biometric authentication. So the main contributions is that  by using simple older methods LBPH can help modern state of the art face regognition to detect presentation attacks according to the results of the tests. This study also worked to adapt this PAD to be suitable for low end edge devices to be able to adapt in an environment where modern solutions are used, which LBPH have.
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Komulainen, J. (Jukka). "Software-based countermeasures to 2D facial spoofing attacks." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526208732.

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Анотація:
Abstract Because of its natural and non-intrusive interaction, identity verification and recognition using facial information is among the most active areas in computer vision research. Unfortunately, it has been shown that conventional 2D face recognition techniques are vulnerable to spoofing attacks, where a person tries to masquerade as another one by falsifying biometric data and thereby gaining an illegitimate advantage. This thesis explores different directions for software-based face anti-spoofing. The proposed approaches are divided into two categories: first, low-level feature descriptors are applied for describing the static and dynamic characteristic differences between genuine faces and fake ones in general, and second, complementary attack-specific countermeasures are investigated in order to overcome the limitations of generic spoof detection schemes. The static face representation is based on a set of well-known feature descriptors, including local binary patterns, Gabor wavelet features and histogram of oriented gradients. The key idea is to capture the differences in quality, light reflection and shading by analysing the texture and gradient structure of the input face images. The approach is then extended to the spatiotemporal domain when both facial appearance and dynamics are exploited for spoof detection using local binary patterns from three orthogonal planes. It is reasonable to assume that no generic spoof detection scheme is able to detect all known, let alone unseen, attacks scenarios. In order to find out well-generalizing countermeasures, the problem of anti-spoofing is broken into two attack-specific sub-problems based on whether the spoofing medium can be detected in the provided view or not. The spoofing medium detection is performed by describing the discontinuities in the gradient structures around the detected face. If the display medium is concealed outside the view, a combination of face and background motion correlation measurement and texture analysis is applied. Furthermore, an open-source anti-spoofing fusion framework is introduced and its system-level performance is investigated more closely in order to gain insight on how to combine different anti-spoofing modules. The proposed spoof detection schemes are evaluated on the latest benchmark datasets. The main findings of the experiments are discussed in the thesis
Tiivistelmä Kasvokuvaan perustuvan henkilöllisyyden tunnistamisen etuja ovat luonnollinen vuorovaikutus ja etätunnistus, minkä takia aihe on ollut erittäin aktiivinen tutkimusalue konenäön tutkimuksessa. Valitettavasti tavanomaiset kasvontunnistustekniikat ovat osoittautuneet haavoittuvaisiksi hyökkäyksille, joissa kameralle esitetään jäljennös kohdehenkilön kasvoista positiivisen tunnistuksen toivossa. Tässä väitöskirjassa tutkitaan erilaisia ohjelmistopohjaisia ratkaisuja keinotekoisten kasvojen ilmaisuun petkuttamisen estämiseksi. Työn ensimmäisessä osassa käytetään erilaisia matalan tason piirteitä kuvaamaan aitojen ja keinotekoisten kasvojen luontaisia staattisia ja dynaamisia eroavaisuuksia. Työn toisessa osassa esitetään toisiaan täydentäviä hyökkäystyyppikohtaisia vastakeinoja, jotta yleispätevien menetelmien puutteet voitaisiin ratkaista ongelmaa rajaamalla. Kasvojen staattisten ominaisuuksien esitys perustuu yleisesti tunnettuihin matalan tason piirteisiin, kuten paikallisiin binäärikuvioihin, Gabor-tekstuureihin ja suunnattujen gradienttien histogrammeihin. Pääajatuksena on kuvata aitojen ja keinotekoisten kasvojen laadun, heijastumisen ja varjostumisen eroavaisuuksia tekstuuria ja gradienttirakenteita analysoimalla. Lähestymistapaa laajennetaan myös tila-aika-avaruuteen, jolloin hyödynnetään samanaikaisesti sekä kasvojen ulkonäköä ja dynamiikkaa irroittamalla paikallisia binäärikuvioita tila-aika-avaruuden kolmelta ortogonaaliselta tasolta. Voidaan olettaa, ettei ole olemassa yksittäistä yleispätevää vastakeinoa, joka kykenee ilmaisemaan jokaisen tunnetun hyökkäystyypin, saati tuntemattoman. Näin ollen työssä keskitytään tarkemmin kahteen hyökkäystilanteeseen. Ensimmäisessä tapauksessa huijausapuvälineen reunoja ilmaistaan analysoimalla gradienttirakenteiden epäjatkuvuuksia havaittujen kasvojen ympäristössä. Jos apuvälineen reunat on piilotettu kameran näkymän ulkopuolelle, petkuttamisen ilmaisu toteutetaan yhdistämällä kasvojen ja taustan liikkeen korrelaation mittausta ja kasvojen tekstuurianalyysiä. Lisäksi työssä esitellään vastakeinojen yhdistämiseen avoimen lähdekoodin ohjelmisto, jonka avulla tutkitaan lähemmin menetelmien fuusion vaikutuksia. Tutkimuksessa esitetyt menetelmät on kokeellisesti vahvistettu alan viimeisimmillä julkisesti saatavilla olevilla tietokannoilla. Tässä väitöskirjassa käydään läpi kokeiden päähavainnot
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5

Jezequel, Loïc. "Vers une détection d'anomalie unifiée avec une application à la détection de fraude." Electronic Thesis or Diss., CY Cergy Paris Université, 2023. http://www.theses.fr/2023CYUN1190.

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Анотація:
La détection d'observation s'écartant d'un cas de référence est cruciale dans de nombreuses applications. Cette problématique est présente dans la détection de fraudes, l'imagerie médicale, voire même la surveillance vidéo avec des données allant d'image aux sons. La détection d'anomalie profonde a été introduite dans cette optique, en modélisant la classe normale et en considérant tout ce qui est significativement différent comme étant anormal. Dans la mesure où la classe anormale n'est pas bien définie, une classification binaire classique manquerait de robustesse et de fiabilité sur des données hors de son domaine d'apprentissage. Néanmoins, les approches de détection d'anomalies les plus performantes se généralisent encore mal à différents types d'anomalies. Aucune méthode ne permet de simultanément détecter des anomalies d'objets à grande échelle, et des anomalies locales à petite échelle.Dans ce contexte, nous introduisons un premier détecteur d'anomalies plus générique par tâche prétexte. Le modèle, nommé OC-MQ, calcule un score d'anomalie en apprenant à résoudre une tâche prétexte complexe sur la classe normale. La tâche prétexte est composée de plusieurs sous-tâches, séparées en tâche discriminatives et génératives, lui permettant de capturer une grande variété de caractéristiques visuelles.Néanmoins, un ensemble de données d'anomalies supplémentaires est en pratique souvent disponible. Dans cette optique, nous explorons deux approches intégrant des données d'anomalie afin de mieux traiter les cas limites. Tout d'abord, nous généralisons le concept de tâche de prétexte au cas semi-supervisé en apprenant aussi dynamiquement la tâche de prétexte avec des échantillons normaux et anormaux. Nous proposons les modèles SadTPS et SadRest, qui apprennent respectivement une tâche prétexte de reconnaissance de TPS et une tâche de restauration d'image. De plus, nous présentons un nouveau modèle de distance d'anomalie, SadCLR, où l'entraînement est stabilisé par une régularisation contrastive sur la direction des représentations apprises. Nous enrichissons davantage les anomalies existantes en générant plusieurs types de pseudo-anomalies.Enfin, nous prolongeons les deux approches précédentes pour les rendre utilisables avec ou sans données d'anomalies. Premièrement, nous introduisons le modèle AnoMem, qui mémorise un ensemble de prototypes normaux à plusieurs échelles en utilisant des couches de Hopfield modernes. Des estimateurs de distance d'anomalie sont ensuite appris sur les disparités entre l'entrée observée et les prototypes normaux. Deuxièmement, nous reformulons les tâches prétextes apprenables afin qu'elles soient apprises uniquement à partir d'échantillons normaux. Notre modèle proposé, HEAT, apprend de manière adverse la tâche prétexte afin de maintenir de bonnes performance sur les échantillons normaux, tout en échouant sur les anomalies. De plus, nous choisissons la distance de Busemann, récemment proposée dans le modèle du disque de Poincaré, pour calculer le score d'anomalie.Des évaluations approfondies sont réalisées pour chaque méthode proposée, incluant des anomalies grossières, fines ou locales avec comme application l'antifraude visage. Les résultats obtenus dépassant l'état de l'art démontrent le succès de nos méthodes
Detecting observations straying apart from a baseline case is becoming increasingly critical in many applications. It is found in fraud detection, medical imaging, video surveillance or even in manufacturing defect detection with data ranging from images to sound. Deep anomaly detection was introduced to tackle this challenge by properly modeling the normal class, and considering anything significantly different as anomalous. Given the anomalous class is not well-defined, classical binary classification will not be suitable and lack robustness and reliability outside its training domain. Nevertheless, the best-performing anomaly detection approaches still lack generalization to different types of anomalies. Indeed, each method is either specialized on high-scale object anomalies or low-scale local anomalies.In this context, we first introduce a more generic one-class pretext-task anomaly detector. The model, named OC-MQ, computes an anomaly score by learning to solve a complex pretext task on the normal class. The pretext task is composed of several sub-tasks allowing it to capture a wide variety of visual cues. More specifically, our model is made of two branches each representing discriminative and generative tasks.Nevertheless, an additional anomalous dataset is in reality often available in many applications and can provide harder edge-case anomalous examples. In this light, we explore two approaches for outlier-exposure. First, we generalize the concept of pretext task to outlier-exposure by dynamically learning the pretext task itself with normal and anomalous samples. We propose two the models SadTPS and SadRest that respectively learn a discriminative pretext task of thin plate transform recognition and generative task of image restoration. In addition, we present a new anomaly-distance model SadCLR, where the training of previously unreliable anomaly-distance models is stabilized by adding contrastive regularization on the representation direction. We further enrich existing anomalies by generating several types of pseudo-anomalies.Finally, we extend the two previous approaches to be usable in both one-class and outlier-exposure setting. Firstly, we introduce the AnoMem model which memorizes a set of multi-scale normal prototypes by using modern Hopfield layers. Anomaly distance estimators are then fitted on the deviations between the input and normal prototypes in a one-class or outlier-exposure manner. Secondly, we generalize learnable pretext tasks to be learned only using normal samples. Our proposed model HEAT adversarially learns the pretext task to be just challenging enough to keep good performance on normal samples, while failing on anomalies. Besides, we choose the recently proposed Busemann distance in the hyperbolic Poincaré ball model to compute the anomaly score.Extensive testing was conducted for each proposed method, varying from coarse and subtle style anomalies to a fraud detection dataset of face presentation attacks with local anomalies. These tests yielded state-of-the-art results, showing the significant success of our methods
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Giunta, Alberto. "Implementazione e analisi comparativa di tecniche di Face Morphing Detection." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17029/.

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Il Face Morphing Attack consiste nel presentare ai portali di Automatic Border Control dei passaporti sostanzialmente validi e regolarmente emessi, ma con fotografie e quindi dati biometrici del volto che ne permettano l’uso da parte di più di un solo soggetto, e quindi da parte di soggetti diversi rispetto al legittimo proprietario del documento. Il campo di ricerca in ambito di Face Morphing Detection è ancora molto giovane e attivo, nonché frammentato: ciascuno studio sull’argomento propone tecniche in qualche modo differenti dalle precedenti e ne verifica l’efficacia su dataset proprietari e costruiti ad hoc da ciascun gruppo di ricercatori. Con il lavoro proposto in questo lavoro di Tesi si cerca di fare maggiore chiarezza sull’efficacia di diversi metodi di Face Morphing Detection noti in letteratura, applicandoli a situazioni e dataset più fedeli alla realtà e facendone un’estensiva analisi comparativa.
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Scherhag, Ulrich Johannes [Verfasser], Dieter W. [Akademischer Betreuer] Fellner, Christoph [Akademischer Betreuer] Busch, and Raymond N. J. [Akademischer Betreuer] Veldhuis. "Face Morphing and Morphing Attack Detection / Ulrich Johannes Scherhag ; Dieter W. Fellner, Christoph Busch, Raymond N. J. Veldhuis." Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1230062467/34.

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Falade, Joannes Chiderlos. "Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.

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La biométrie est de plus en plus utilisée à des fins d’identification compte tenu de la relation étroite entre la personne et son identifiant (comme une empreinte digitale). Nous positionnons cette thèse sur la problématique de l’identification d’individus à partir de ses empreintes digitales. L’empreinte digitale est une donnée biométrique largement utilisée pour son efficacité, sa simplicité et son coût d’acquisition modeste. Les algorithmes de comparaison d’empreintes digitales sont matures et permettent d’obtenir en moins de 500 ms un score de similarité entre un gabarit de référence (stocké sur un passeport électronique ou une base de données) et un gabarit acquis. Cependant, il devient très important de déterminer l'identité d'un individu contre une population entière en un temps très court (quelques secondes). Ceci représente un enjeu important compte tenu de la taille de la base de données biométriques (contenant un ensemble d’individus de l’ordre d’un pays). Par exemple, avant de délivrer un nouveau passeport à un individu qui en fait la demande, il faut faire une recherche d'identification sur la base des données biométriques du pays afin de s'assurer que ce dernier n'en possède pas déjà un autre mais avec les mêmes empreintes digitales (éviter les doublons). Ainsi, la première partie du sujet de cette thèse concerne l’identification des individus en utilisant les empreintes digitales. D’une façon générale, les systèmes biométriques ont pour rôle d’assurer les tâches de vérification (comparaison 1-1) et d’identification (1-N). Notre sujet se concentre sur l’identification avec N étant à l’échelle du million et représentant la population d’un pays par exemple. Dans le cadre de nos travaux, nous avons fait un état de l’art sur les méthodes d’indexation et de classification des bases de données d’empreintes digitales. Nous avons privilégié les représentations binaires des empreintes digitales pour indexation. Tout d’abord, nous avons réalisé une étude bibliographique et rédigé un support sur l’état de l’art des techniques d’indexation pour la classification des empreintes digitales. Ensuite, nous avons explorer les différentes représentations des empreintes digitales, puis réaliser une prise en main et l’évaluation des outils disponibles à l’imprimerie Nationale (IN Groupe) servant à l'extraction des descripteurs représentant une empreinte digitale. En partant de ces outils de l’IN, nous avons implémenté quatre méthodes d’identification sélectionnées dans l’état de l’art. Une étude comparative ainsi que des améliorations ont été proposées sur ces méthodes. Nous avons aussi proposé une nouvelle solution d'indexation d'empreinte digitale pour réaliser la tâche d’identification qui améliore les résultats existant. Les différents résultats sont validés sur des bases de données de tailles moyennes publiques et nous utilisons le logiciel Sfinge pour réaliser le passage à l’échelle et la validation complète des stratégies d’indexation. Un deuxième aspect de cette thèse concerne la sécurité. Une personne peut avoir en effet, la volonté de dissimuler son identité et donc de mettre tout en œuvre pour faire échouer l’identification. Dans cette optique, un individu peut fournir une empreinte de mauvaise qualité (portion de l’empreinte digitale, faible contraste en appuyant peu sur le capteur…) ou fournir une empreinte digitale altérée (empreinte volontairement abîmée, suppression de l’empreinte avec de l’acide, scarification…). Il s'agit donc dans la deuxième partie de cette thèse de détecter les doigts morts et les faux doigts (silicone, impression 3D, empreinte latente) utilisés par des personnes mal intentionnées pour attaquer le système. Nous avons proposé une nouvelle solution de détection d'attaque basée sur l'utilisation de descripteurs statistiques sur l'empreinte digitale. Aussi, nous avons aussi mis en place trois chaînes de détections des faux doigts utilisant les techniques d'apprentissages profonds
Biometrics are increasingly used for identification purposes due to the close relationship between the person and their identifier (such as fingerprint). We focus this thesis on the issue of identifying individuals from their fingerprints. The fingerprint is a biometric data widely used for its efficiency, simplicity and low cost of acquisition. The fingerprint comparison algorithms are mature and it is possible to obtain in less than 500 ms a similarity score between a reference template (enrolled on an electronic passport or database) and an acquired template. However, it becomes very important to check the identity of an individual against an entire population in a very short time (a few seconds). This is an important issue due to the size of the biometric database (containing a set of individuals of the order of a country). Thus, the first part of the subject of this thesis concerns the identification of individuals using fingerprints. Our topic focuses on the identification with N being at the scale of a million and representing the population of a country for example. Then, we use classification and indexing methods to structure the biometric database and speed up the identification process. We have implemented four identification methods selected from the state of the art. A comparative study and improvements were proposed on these methods. We also proposed a new fingerprint indexing solution to perform the identification task which improves existing results. A second aspect of this thesis concerns security. A person may want to conceal their identity and therefore do everything possible to defeat the identification. With this in mind, an individual may provide a poor quality fingerprint (fingerprint portion, low contrast by lightly pressing the sensor...) or provide an altered fingerprint (impression intentionally damaged, removal of the impression with acid, scarification...). It is therefore in the second part of this thesis to detect dead fingers and spoof fingers (silicone, 3D fingerprint, latent fingerprint) used by malicious people to attack the system. In general, these methods use machine learning techniques and deep learning. Secondly, we proposed a new presentation attack detection solution based on the use of statistical descriptors on the fingerprint. Thirdly, we have also build three presentation attacks detection workflow for fake fingerprint using deep learning. Among these three deep solutions implemented, two come from the state of the art; then the third an improvement that we propose. Our solutions are tested on the LivDet competition databases for presentation attack detection
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Pancisi, Emanuele. "Riconoscimento di volti morphed: un approccio basato su Deep Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23154/.

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La pervasività dei sistemi informatici nella vita di ogni giorno richiede di riporre grande attenzione verso il tema della sicurezza informatica, specialmente in tutti quei contesti in cui la violazione di questi sistemi può portare a conseguenze sociali rilevanti. Questo è particolarmente importante nel caso applicativo di controllo automatico degli accessi basato su sistemi di riconoscimento biometrici. Recentemente, gli attacchi basati su tecniche di face morphing hanno suscitato l'interesse della comunità scientifica. É stato dimostrato, infatti, che questi rappresentano una seria e concreta minaccia in varie applicazioni basate sulla verifica automatica dell'identità attraverso sistemi di riconoscimento facciale. Lo scenario considerato è quello dei controlli realizzati nei gate presenti all'interno degli aeroporti internazionali che, per velocizzare la circolazione dei passeggeri, verificano automaticamente se il volto di un soggetto corrisponde a quello contenuto all'interno del suo passaporto elettronico (eMRTD). Attraverso una procedura di morphing due soggetti possono condividere lo stesso documento legale violando il principio fondamentale di collegamento biunivoco tra un individuo e il suo documento identificativo. A questo proposito un soggetto senza precedenti penali potrebbe richiedere, nelle strutture preposte, il passaporto elettronico presentando una foto morphed con il volto di un criminale che successivamente potrà utilizzare il documento per eludere i controlli d'identità. Per questi motivi è forte il bisogno di algoritmi capaci di rilevare in maniera accurata e automatica immagini morphed. L'obiettivo di questo lavoro di tesi è quello di comprendere meglio il problema del face morphing e affrontarlo, nei diversi scenari, proponendo nuovi algoritmi basati su deep learning, ponendo particolare attenzione alla realizzazione di esperimenti rilevanti e sull'analisi critica dei metodi proposti e dei risultati sperimentali ottenuti.
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Nóbrega, Murilo Leite. "Explainable and Interpretable Face Presentation Attack Detection Methods." Master's thesis, 2021. https://hdl.handle.net/10216/139294.

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Decision support systems based on machine learning (ML) techniques are excelling in most artificial intelligence (AI) fields, over-performing other AI methods, as well as humans. However, challenges still exist that do not favour the dominance of AI in some applications. This proposal focuses on a critical one: lack of transparency and explainability, reducing trust and accountability of an AI system. The fact that most AI methods still operate as complex black boxes, makes the inner processes which sustain their predictions still unattainable. The awareness around these observations foster the need to regulate many sensitive domains where AI has been applied in order to interpret, explain and audit the reliability of the ML based systems. Although modern-day biometric recognition (BR) systems are already benefiting from the performance gains achieved with AI (which can account for and learn subtle changes in the person to be authenticated or statistical mismatches between samples), it is still in the dark ages of black box models, without reaping the benefits of the mismatches between samples), it is still in the dark ages of black box models, without reaping the benefits of the XAI field. This work will focus on studying AI explainability in the field of biometrics focusing in particular use cases in BR, such as verification/ identification of individuals and liveness detection (LD) (aka, antispoofing). The main goals of this work are: i) to become acquainted with the state-of-the-art in explainability and biometric recognition and PAD methods; ii) to develop an experimental work xxxxx Tasks 1st semester (1) Study of the state of the art- bibliography review on state of the art for presentation attack detection (2) Get acquainted with the previous work of the group in the topic (3) Data preparation and data pre-processing (3) Define the experimental protocol, including performance metrics (4) Perform baseline experiments (5) Write monography Tasks 2nd semester (1) Update on the state of the art (2) Data preparation and data pre-processing (3) Propose and implement a methodology for interpretability in biometrics (4) Evaluation of the performance and comparison with baseline and state of the art approaches (5) Dissertation writing Referências bibliográficas principais: (*) [Doshi17] B. Kim and F. Doshi-Velez, "Interpretable machine learning: The fuss, the concrete and the questions," 2017 [Mol19] Christoph Molnar. Interpretable Machine Learning. 2019 [Sei18] C. Seibold, W. Samek, A. Hilsmann, and P. Eisert, "Accurate and robust neural networks for security related applications exampled by face morphing attacks," arXiv preprint arXiv:1806.04265, 2018 [Seq20] Sequeira, Ana F., João T. Pinto, Wilson Silva, Tiago Gonçalves and Cardoso, Jaime S., "Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated?", 8th IWBF2020 [Wilson18] W. Silva, K. Fernandes, M. J. Cardoso, and J. S. Cardoso, "Towards complementary explanations using deep neural networks," in Understanding and Interpreting Machine Learning in MICA. Springer, 2018 [Wilson19] W. Silva, K. Fernandes, and J. S. Cardoso, "How to produce complementary explanations using an Ensemble Model," in IJCNN. 2019 [Wilson19A] W. Silva, M. J. Cardoso, and J. S. Cardoso, "Image captioning as a proxy for Explainable Decisions" in Understanding and Interpreting Machine Learning in MICA, 2019 (Submitted)
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Книги з теми "Face presentation attack detection"

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. Multi-Modal Face Presentation Attack Detection. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. Advances in Face Presentation Attack Detection. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32906-7.

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Li, Stan Z., Escalera, Jun Wan, Guodong Guo, and Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Morgan & Claypool Publishers, 2020.

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Li, Stan Z., Jun Wan, Sergio Escalera, Guodong Guo, and Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Springer International Publishing AG, 2020.

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5

Li, Stan Z., Escalera, Jun Wan, Guodong Guo, and Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Morgan & Claypool Publishers, 2020.

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6

Li, Stan Z., Jun Wan, Sergio Escalera, Guodong Guo, and Hugo Jair Escalante. Multi-Modal Face Presentation Attack Detection. Morgan & Claypool Publishers, 2020.

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7

Nixon, Mark S., Nicholas Evans, Julian Fierrez, and Sébastien Marcel. Handbook of Biometric Anti-Spoofing: Presentation Attack Detection. Springer, 2019.

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8

Nicholas, Evans. Handbook of Biometric Anti-Spoofing: Presentation Attack Detection and Vulnerability Assessment. Springer, 2023.

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Частини книг з теми "Face presentation attack detection"

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Review of Participants’ Methods." In Multi-Modal Face Presentation Attack Detection, 19–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4_3.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Conclusions and Future Works." In Multi-Modal Face Presentation Attack Detection, 59–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4_5.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Challenge Results." In Multi-Modal Face Presentation Attack Detection, 37–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4_4.

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Hernandez-Ortega, Javier, Julian Fierrez, Aythami Morales, and Javier Galbally. "Introduction to Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing, 187–206. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_9.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Face Presentation Attack Detection (PAD) Challenges." In Synthesis Lectures on Computer Vision, 17–35. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32906-7_2.

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Bhattacharjee, Sushil, Amir Mohammadi, André Anjos, and Sébastien Marcel. "Recent Advances in Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing, 207–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_10.

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Komulainen, Jukka, Zinelabidine Boulkenafet, and Zahid Akhtar. "Review of Face Presentation Attack Detection Competitions." In Handbook of Biometric Anti-Spoofing, 291–317. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_14.

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Yu, Zitong, Jukka Komulainen, Xiaobai Li, and Guoying Zhao. "Review of Face Presentation Attack Detection Competitions." In Handbook of Biometric Anti-Spoofing, 287–336. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5288-3_12.

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Liu, Yaojie, Joel Stehouwer, Amin Jourabloo, Yousef Atoum, and Xiaoming Liu. "Presentation Attack Detection for Face in Mobile Phones." In Selfie Biometrics, 171–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26972-2_8.

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Liu, Si-Qi, and Pong C. Yuen. "Recent Progress on Face Presentation Attack Detection of 3D Mask Attack." In Handbook of Biometric Anti-Spoofing, 231–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5288-3_10.

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Тези доповідей конференцій з теми "Face presentation attack detection"

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Denisova, Anna. "An improved simple feature set for face presentation attack detection." In WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022. Západočeská univerzita, 2022. http://dx.doi.org/10.24132/csrn.3201.3.

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Presentation attacks are weak points of facial biometrical authentication systems. Although several presentation attack detection methods were developed, the best of them require a sufficient amount of training data and rely on computationally intensive deep learning based features. Thus, most of them have difficulties with adaptation to new types of presentation attacks or new cameras. In this paper, we introduce a method for face presentation attack detection with low requirements for training data and high efficiency for a wide range of spoofing attacks. The method includes feature extraction and binary classification stages. We use a combination of simple statistical and texture features and describe the experimental results of feature adjustment and selection. We validate the proposed method using WMCA dataset. The experiments showed that the proposed features decrease the average classification error in comparison with the RDWT-Haralick-SVM method and demonstrate the best performance among non-CNN-based methods.
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Nandy, Anubhab, and Satish Kumar Singh. "Face Spoofing and Presentation Attack Detection." In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2022. http://dx.doi.org/10.1109/aic55036.2022.9848980.

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Fang, Hao, Ajian Liu, Jun Wan, Sergio Escalera, Hugo Jair Escalante, and Zhen Lei. "Surveillance Face Presentation Attack Detection Challenge." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00677.

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Baweja, Yashasvi, Poojan Oza, Pramuditha Perera, and Vishal M. Patel. "Anomaly Detection-Based Unknown Face Presentation Attack Detection." In 2020 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2020. http://dx.doi.org/10.1109/ijcb48548.2020.9304935.

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Damer, Naser, and Kristiyan Dimitrov. "Practical View on Face Presentation Attack Detection." In British Machine Vision Conference 2016. British Machine Vision Association, 2016. http://dx.doi.org/10.5244/c.30.112.

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Sanghvi, Nilay, Sushant Kumar Singh, Akshay Agarwal, Mayank Vatsa, and Richa Singh. "MixNet for Generalized Face Presentation Attack Detection." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412123.

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"Face Presentation Attack Detection via Spatiotemporal Autoencoder." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302402.

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Nowara, Ewa Magdalena, Ashutosh Sabharwal, and Ashok Veeraraghavan. "PPGSecure: Biometric Presentation Attack Detection Using Photopletysmograms." In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, 2017. http://dx.doi.org/10.1109/fg.2017.16.

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Mirzaalian, Hengameh, Mohamed E. Hussein, Leonidas Spinoulas, Jonathan May, and Wael Abd-Almageed. "Explaining Face Presentation Attack Detection Using Natural Language." In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). IEEE, 2021. http://dx.doi.org/10.1109/fg52635.2021.9667024.

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Agarwal, Akshay, Akarsha Sehwag, Richa Singh, and Mayank Vatsa. "Deceiving Face Presentation Attack Detection via Image Transforms." In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM). IEEE, 2019. http://dx.doi.org/10.1109/bigmm.2019.00018.

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