Multi-Channel Cross Modal Detection of Synthetic Face Images

Synthetically generated face images have shown to be indistinguishable from real images by humans and as such can lead to a lack of trust in digital content as they can, for instance, be used to spread misinformation. Therefore, the need to develop algorithms for detecting entirely synthetic face images is apparent. Of interest are images generated by state-of-the-art deep learning-based models, as these exhibit a high level of visual realism. Recent works have demonstrated that detecting such synthetic face images under realistic circumstances remains difficult as new and improved generative models are proposed with rapid speed and arbitrary image post-processing can be applied. In this work, we propose a multi-channel architecture for detecting entirely synthetic face images which analyses information both in the frequency and visible spectra using Cross Modal Focal Loss. We compare the proposed architecture with several related architectures trained using Binary Cross Entropy and show in cross-model experiments that the proposed architecture supervised using Cross Modal Focal Loss, in general, achieves most competitive performance

A New Biometric Template Protection using Random Orthonormal Projection and Fuzzy Commitment

Biometric template protection is one of most essential parts in putting a biometric-based authentication system into practice. There have been many researches proposing different solutions to secure biometric templates of users. They can be categorized into two approaches: feature transformation and biometric cryptosystem. However, no one single template protection approach can satisfy all the requirements of a secure biometric-based authentication system. In this work, we will propose a novel hybrid biometric template protection which takes benefits of both approaches while preventing their limitations. The experiments demonstrate that the performance of the system can be maintained with the support of a new random orthonormal project technique, which reduces the computational complexity while preserving the accuracy. Meanwhile, the security of biometric templates is guaranteed by employing fuzzy commitment protocol.Comment: 11 pages, 6 figures, accepted for IMCOM 201

Context-based texture analysis for secure revocable iris-biometric key generation

In this work we present an iris-biometric cryptosystem. Based on the idea of exploiting the most reliable components of iriscodes, cryptographic keys are extracted, long enough to be applied in common cryptosystems. The main benefit of our system is that cryptographic keys are directly derived from biometric data, thus, neither plain biometric data nor encrypted biometric data has to be stored in templates. Yet, we provide fully revocable cryptographic keys. Experimental results emphasize the worthiness of our approach

Mobile Contactless Fingerprint Presentation Attack Detection: Generalizability and Explainability

Contactless fingerprint recognition is an emerging biometric technology that has several advantages over contact-based schemes, such as improved user acceptance and fewer hygienic concerns. Like for most other biometrics, Presentation Attack Detection (PAD) is crucial to preserving the trustworthiness of contactless fingerprint recognition methods. For many contactless biometric characteristics, Convolutional Neural Networks (CNNs) represent the state-of-the-art of PAD algorithms. For CNNs, the ability to accurately classify samples that are not included in the training is of particular interest, since these generalization capabilities indicate robustness in real-world scenarios. In this work, we focus on the generalizability and explainability aspects of CNN-based contactless fingerprint PAD methods. Based on previously obtained findings, we selected four CNN-based methods for contactless fingerprint PAD: two PAD methods designed for other biometric characteristics, an algorithm for contact-based fingerprint PAD and a general-purpose ResNet18. For our evaluation, we use four databases and partition them using Leave-One-Out (LOO) protocols. Furthermore, the generalization capability to a newly captured database is tested. Moreover, we explore t-SNE plots as a means of explainability to interpret our results in more detail. The low D-EERs obtained from the LOO experiments (below 0.1% D-EER for every LOO group) indicate that the selected algorithms are well-suited for the particular application. However, with an D-EER of 4.14%, the generalization experiment still has room for improvement

Устройство для перемещения датчиков в магнитном поле малогабаритного бетатрона

Рассматривается возможность увеличения точности измерений характеристик магнитного поля посредством более точной установки датчиков в исследуемой точке

Prolactin

During an oral glucose tolerance test (OGTT) glucose and insulin levels were measured in 26 patients with prolactin-producing pituitary tumours without growth hormone excess. Basal glucose and insulin levels did not differ from the values of an age-matched control group. After glucose load the hyperprolactinaemic patients showed a decrease in glucose tolerance and a hyperinsulinaemia. Bromocriptine (CB 154), which suppressed PRL, improved glucose tolerance and decreased insulin towards normal in a second OGTT. — Human PRL or CB 154 had no significant influence on insulin release due to glucose in the perfused rat pancreas. — These findings suggest a diabetogenic effect of PRL. CB 154 might be a useful drug in improving glucose utilization in hormone-active pituitary tumours

Research in graphic design

An invited and reviewed opinion piece about the current state of research in graphic/communication design