Vulnerabilities of Unattended Face Verification Systems to Facial Components-based Presentation Attacks: An Empirical Study

As face presentation attacks (PAs) are realistic threats for unattended face verification systems, face presentation attack detection (PAD) has been intensively investigated in past years, and the recent advances in face PAD have significantly reduced the success rate of such attacks. In this article, an empirical study on a novel and effective face impostor PA is made. In the proposed PA, a facial artifact is created by using the most vulnerable facial components, which are optimally selected based on the vulnerability analysis of different facial components to impostor PAs. An attacker can launch a face PA by presenting a facial artifact on his or her own real face. With a collected PA database containing various types of artifacts and presentation attack instruments (PAIs), the experimental results and analysis show that the proposed PA poses a more serious threat to face verification and PAD systems compared with the print, replay, and mask PAs. Moreover, the generalization ability of the proposed PA and the vulnerability analysis with regard to commercial systems are also investigated by evaluating unknown face verification and real-world PAD systems. It provides a new paradigm for the study of face PAs.

Verification Systems and Programs in Regional Television Stations That Are Members of the CIRCOM Network

Disinformation and the proliferation of fake news are global problems that affect the stability of democracies throughout the world. The capacity of distorted information to interfere in election processes or in political agendas has led different actors to create verification initiatives, which operate in partnership with the mass media. Recently, during the 2020 health crisis, false information has proved to have damaging power not only at the levels of politics or communication, but also at a health level. Therefore, the social need to access reliable and quality information, as well as verified information aimed at eradicating hoaxes, becomes evident. This paper focuses on the European context, analyzing the relationship between active verifiers and television stations that are members of the CIRCOM Network, considering their strategies and verification programs. Using a qualitative methodology an exploratory study has been carried out, mapping initiatives and stations by assessing their contribution of verified information to society.

A Supervised Learning Method for Improving the Generalization of Speaker Verification Systems by Learning Metrics from a Mean Teacher

The majority of recent speaker verification tasks are studied under open-set evaluation scenarios considering real-world conditions. The characteristics of these tasks imply that the generalization towards unseen speakers is a critical capability. Thus, this study aims to improve the generalization of the system for the performance enhancement of speaker verification. To achieve this goal, we propose a novel supervised-learning-method-based speaker verification system using the mean teacher framework. The mean teacher network refers to the temporal averaging of deep neural network parameters, which can produce a more accurate, stable representations than fixed weights at the end of training and is conventionally used for semi-supervised learning. Leveraging the success of the mean teacher framework in many studies, the proposed supervised learning method exploits the mean teacher network as an auxiliary model for better training of the main model, the student network. By learning the reliable intermediate representations derived from the mean teacher network as well as one-hot speaker labels, the student network is encouraged to explore more discriminative embedding spaces. The experimental results demonstrate that the proposed method relatively reduces the equal error rate by 11.61%, compared to the baseline system.

PROSPECTIVE APPROACHES TO ASSESSING FOREIGN ECONOMIC OPERATIONS OF COMPANIES THROUGH THE USE OF INFORMATION TECHNOLOGY

The article discusses some aspects of interaction between companies engaged in foreign economic operations and customs authorities. In particular, the main attention is paid to the development of digitalization processes of interaction between participants in foreign economic activity and customs authorities through the introduction of self-verification systems for the company's foreign economic operations and customs monitoring. The article has developed an automated model for assessing the company's foreign economic activity, and also investigated the main aspects of the institution of customs monitoring. Particular attention is paid to the use of the customs monitoring system as a tool for automating control over the activities of authorized economic operators.

K-Nearest Neighbour and Dynamic Time Warping for Online Signature Verification

Online signatures are one of the most commonly used biometrics. Several verification systems and public databases were presented in this field. This paper presents a combination of knearest neighbor and dynamic time warping algorithms as a verification system using the recently published DeepSignDB database. Our algorithm was applied on both finger and stylus input signatures which represent both office and mobile scenarios. The system was first tested on the development set of the database. It achieved an error rate of 6.04% for the stylus input signatures, 5.20% for the finger input signatures, and 6.00% for a combination of both types. The system was also applied to the evaluation set of the database and achieved very promising results, especially for finger input signatures.

Online signature verification using signature down-sampling and signer-dependent sampling frequency

Online signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.

Feature Focus: Towards Explainable and Transparent Deep Face Morphing Attack Detectors

Detecting morphed face images has become an important task to maintain the trust in automated verification systems based on facial images, e.g., at automated border control gates. Deep Neural Network (DNN)-based detectors have shown remarkable results, but without further investigations their decision-making process is not transparent. In contrast to approaches based on hand-crafted features, DNNs have to be analyzed in complex experiments to know which characteristics or structures are generally used to distinguish between morphed and genuine face images or considered for an individual morphed face image. In this paper, we present Feature Focus, a new transparent face morphing detector based on a modified VGG-A architecture and an additional feature shaping loss function, as well as Focused Layer-wise Relevance Propagation (FLRP), an extension of LRP. FLRP in combination with the Feature Focus detector forms a reliable and accurate explainability component. We study the advantages of the new detector compared to other DNN-based approaches and evaluate LRP and FLRP regarding their suitability for highlighting traces of image manipulation from face morphing. To this end, we use partial morphs which contain morphing artifacts in predefined areas only and analyze how much of the overall relevance each method assigns to these areas.