Um estudo comparativo de contramedidas para detectar ataques de spoofing em sistemas de autenticação de faces

Orientador: José Mario De MartinoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O Resumo poderá ser visualizado no texto completo da tese digitalAbstract: The complete Abstract is available with the full electronic document.MestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Fairness in Biometrics: a figure of merit to assess biometric verification systems

Machine learning-based (ML) systems are being largely deployed since the last decade in a myriad of scenarios impacting several instances in our daily lives. With this vast sort of applications, aspects of fairness start to rise in the spotlight due to the social impact that this can get in minorities. In this work aspects of fairness in biometrics are addressed. First, we introduce the first figure of merit that is able to evaluate and compare fairness aspects between multiple biometric verification systems, the so-called Fairness Discrepancy Rate (FDR). A use case with two synthetic biometric systems is introduced and demonstrates the potential of this figure of merit in extreme cases of fair and unfair behavior. Second, a use case using face biometrics is presented where several systems are evaluated compared with this new figure of merit using three public datasets exploring gender and race demographics.Comment: 11 page

Learning How To Recognize Faces In Heterogeneous Environments

Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades. Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, such as in border control and in less critical ones, such as to unlock mobile phones. However, the lack of cooperation from the subject and variations on the pose, occlusion and illumination are still open problems and significantly affect error rates. Another challenge that arose recently in face recognition research is the ability of matching faces from different image domains. Use cases encompass the matching between Visual Light images (VIS) with Near infra-red images (NIR), Visual Light images (VIS) with Thermograms or Depth maps. This match can occur even in situations where no real face exists, such as matching using sketches. This task is so called Heterogeneous Face Recognition. The key difficulty in the comparison of faces in heterogeneous conditions is that images from the same subject may differ in appearance due to changes in image domain. In this thesis we address this problem of Heterogeneous Face Recognition (HFR). Our contributions are four-fold. First, we analyze the applicability of crafted features used in face recognition in the HFR task. Second, still working with crafted features, we propose that the variability between two image domains can be suppressed with a linear shift in the Gaussian Mixture Model (GMM) mean subspace. That encompasses inter-session variability (ISV) modeling. Third, we propose that high level features of Deep Convolutional Neural Networks trained on Visual Light images are potentially domain independent and can be used to encode faces sensed in different image domains. Fourth, large-scale experiments are conducted on several HFR databases, covering various image domains showing competitive performances. Moreover, the implementation of all the proposed techniques are integrated into a collaborative open source software library called Bob that enforces fair evaluations and encourages reproducible research

Desdobramentos jurídicos da regulação de drones no Brasil, os perigos do seu uso pelo Estado e a necessidade de equacionamento de direitos com base na análise de 3 casos concretos dos Estados Unidos

Trabalho de Conclusão de Curso (graduação) — Universidade de Brasília, Faculdade de Direito, 2021.A evolução do uso de drones não somente pela sociedade civil, mas também pelo Estado, é um enorme desafio para o Direito. Lidar com os desdobramentos jurídicos do seu uso no mundo real é uma tarefa extremamente difícil, principalmente num contexto de evoluções que acontecem tão rapidamente. Se já existem vários problemas gerados pelos drones nas relações entre Nações em contexto de guerra e entre civis nas suas relações interpessoais, os problemas se tornam ainda mais complexos quando se trata da relação entre Administração Pública e seus administrados, pois sempre haverá um desequilíbrio de poderes, já que o Estado é soberano. Os riscos de dronificação do poder e um hipervigilância são enormes e muitas vezes fatais. Portanto, os problemas daí decorrentes carecem de um equilíbrio, um equacionamento de direitos, pois faz sentido a sociedade civil abrir mão de parte de direitos fundamentais em nome do interesse público, mas não de maneira absoluta. É preciso haver limites e razoabilidade. Assim, a atuação estatal com o uso de drones sempre deve ser ponderada, já que é possível violar alguns direitos ao mesmo tempo em que se afirma outros.The legal breakdowns of using drones by the civil society and the State are a demanding task for the law to solve. Beyond wars and interpersonal relations, those issues get even more harder when it comes to the relationship between the Public Administration and its citizens, because of the equation of rights and the sovereignty of the State. The civil society for example surrenders parts of the fundamental rights for the public interest, even if not in an absolute way. Considering that the risks of "dronification" of power and hypervigilance are numerous and oftentimes fatal, it is necessary a balance, and an equation of rights in order to stablish its limits and reasonableness. Therefore, since it is possible violating some rights and claiming other at the same time, the usage of drones by the State should be guided by no partiality/tendency

Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues

Automatic face recognition is a research area with high popularity. Many different face recognition algorithms have been proposed in the last thirty years of intensive research in the field. With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researchers have concentrated effort on creating better models under this paradigm. From the year 2015, state-of-the-art face recognition has been rooted in deep learning models. Despite the availability of large-scale and diverse datasets for evaluating the performance of face recognition algorithms, many of the modern datasets just combine different factors that influence face recognition, such as face pose, occlusion, illumination, facial expression and image quality. When algorithms produce errors on these datasets, it is not clear which of the factors has caused this error and, hence, there is no guidance in which direction more research is required. This work is a followup from our previous works developed in 2014 and eventually published in 2016, showing the impact of various facial aspects on face recognition algorithms. By comparing the current state-of-the-art with the best systems from the past, we demonstrate that faces under strong occlusions, some types of illumination, and strong expressions are problems mastered by deep learning algorithms, whereas recognition with low-resolution images, extreme pose variations, and open-set recognition is still an open problem. To show this, we run a sequence of experiments using six different datasets and five different face recognition algorithms in an open-source and reproducible manner. We provide the source code to run all of our experiments, which is easily extensible so that utilizing your own deep network in our evaluation is just a few minutes away

Can face anti-spoofing countermeasures work in a real world scenario?

User authentication is an important step to protect in- formation and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipments. This article assesses how well existing face anti-spoofing countermeasures can work in a more realistic condition. Experiments carried out with two freely available video databases (Replay Attack Database and CASIA Face Anti-Spoofing Database) show low generalization and possible database bias in the evaluated countermeasures. To generalize and deal with the diversity of attacks in a real world scenario we introduce two strategies that show promising results

Face liveness detection using dynamic texture

User authentication is an important step to protect information, and in this context, face biometrics is potentially advantageous. Face biometrics is natural, intuitive, easy to use, and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using cheap low-tech equipment. This paper introduces a novel and appealing approach to detect face spoofing using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern operator. The key idea of the approach is to learn and detect the structure and the dynamics of the facial micro-textures that characterise real faces but not fake ones. We evaluated the approach with two publicly available databases (Replay-Attack Database and CASIA Face Anti-Spoofing Database). The results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database2014This work has been performed within the context of the TABULA RASA project, part of the 7th Framework Research Programme of the European Union (EU), under the grant agreement number 257289. The financial support of FUNTTEL (Brazilian Telecommunication Technological Development Fund), Academy of Finland and Infotech Oulu Doctoral Program is also gratefully acknowledg

LBP-TOP based countermeasure against face spoofing attacks

User authentication is an important step to protect information and in this eld face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP-TOP operator combining both space and time information into a single multiresolution texture descriptor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HTER) improvement from 15:16% to 7:60%

Antibacterial and coagulant potential of vegetable extracts in the alternative treatment of greywater

The reuse of greywater (GW) from some domestic activities is considered a sustainable technique for water saving, however, GW have high turbidity and bacteriological load. Therefore, this study aimed to evaluate the use of extracts obtained from Citrus aurantifolia (Christm.) Swingle in the alternative treatment of GW, aiming at reducing turbidity and bacteriological load.  For this, extracts obtained from epicarp, mesocarp, endocarp and whole fruit were prepared from three extraction methods. Posteriorly, they were applied to GW samples, being monitored the turbidity and heterotrophic bacteria at different time intervals. All extracts showed coagulant and antibacterial properties, reducing turbidity (39-88%) and heterotrophic bacteria (66-93%) after 24 hours of treatment. Thus, the evaluated extracts become an alternative for use in GW reuse systems instead of synthetic chemical agents, presenting advantages such as biodegradability, ease of access, biodegradability and low toxicity of the sludge generated.The reuse of greywater (GW) from some domestic activities is considered a sustainable technique for water-saving; however, GW has high turbidity and bacteriological load. Therefore, this study aimed to evaluate the use of extracts obtained from Citrus aurantifolia (Christm.) Swingle in the alternative treatment of GW, aiming at reducing turbidity and bacteriological load.  Extracts obtained from epicarp, mesocarp, endocarp, and whole fruit were prepared from three extraction methods. Posteriorly, they were applied to GW samples, monitoring the turbidity and heterotrophic bacteria at different time intervals. All extracts showed coagulant and antibacterial properties, reducing turbidity (39-88%) and heterotrophic bacteria (66-93%) after 24 h of the treatment. The plant extracts become an alternative for use in GW reuse systems instead of synthetic chemical agents, with biodegradability, ease of access, and low toxicity of the sludge generated