1,720,960 research outputs found
Convolutional Neural Networks to Protect Against Spoofing Attacks on Biometric Face Authentication
No full textModern technologies of authentication and authorization of access play a significant role in ensuring the protection of information in various practical applications. We consider the most convenient and used in modern mobile gadgets face authentication, ie when the primary information to provide access are certain features of biometric images of the user’s face. Most of the systems use intelligent processing of biometric images, in particular, artificial intelligence technology and deep learning. But at the same time, as always in cybersecurity, technologies for violating biometric authentication are being studied and researched. In particular, to date, the most common attack is substitution (spoofing), ie when attackers use pre-recorded biometric images to gain unauthorized access to critical information. For example, this could be a photo and/or video image of a person used to unlock their smartphone. Protection against such attacks is very difficult, because it involves the development and study of technologies for detecting signs of life. The most promising in this direction are artificial intelligence techniques, in particular, convolutional neural networks (CNN). This is the practical application of intelligent processing of biometric images and is studied in this article. We review various CNN settings and configurations and experimentally investigate their effect on the effectiveness of signs of life detection. For this purpose, success and failure indicators of the first and second kind are used, which are estimated by the values of cross entropy. These are reliable and reproducible indicators that characterize the effectiveness of protection against spoofing attacks on biometric authentication on the face. The world-famous TensorFlow and OpenCV libraries are used for field experiments, photos and videos of various users are used as source data, including Replay-Attack Database from Idiap Research Institute
Performance Evaluation of the Classic McEliece Key Encapsulation Algorithm
No full textIn late 2016, the US NIST announced a post-quantum cryptography open competition to select public-key cryptoalgorithms (digital signature, asymmetric encryption, and key encapsulation) suitable for use when quantum computing is widely available. Based on the results of the 2nd round of the competition, several solutions were selected that could potentially be standardized as post-quantum cryptographic algorithms. In this article, we are looking at one of the bids based on codes. The Classic McEliece key encapsulation algorithm is a variant of the well-known (over 40 years) public key cryptosystem in codes. We investigate the various characteristics of the Classic McEliece, in particular, we provide estimates of its performance using various computing platforms and testing technologies
Design of the Residual Adder of Two Numbers
No full textIt is known that an important and topical scientific and applied problem is the problem of constructing the structure of an adder operating in the residual number system (RNS). Such a non-positional adder for an arbitrary value mi of the RNS modulus is a sequential set of n= log 2 (m i-1)+1] binary one-bit adders (OBA), united by connections, like connections of positional binary adders. The use of additional connections made it possible to create an adder that implements the operation of adding two residues. A set of k adders modulo is an adder of two numbers in RNS. Specific examples of constructing structures of binary adders for various values of the RNS moduli are given
Artificial Intelligence and Number System in Residual Classes
No full textThis article discusses a model of the process of information processing by the human brain, based on the assumption that the storage and processing of information is carried out in a non-positional number system in residual classes (RNS). When accepting the hypothesis about the holographic principle of information processing by the human brain, the expediency and effectiveness of building artificial intelligence systems based on the information processing model in the RNS is obvious. This is due to the fact that the principles and methods of information processing in the RNS are in good agreement with modern concepts and ideas about the process of information processing by the human brain. The accuracy of the description (representation) of the information object G depends on the number and values of the RNS bases. So, the larger the number of RNS bases and the larger they are in value, the more accurately the information object G is described by means of frames. This fact confirms the expediency of using the RNS
Biometric authentication using convolutional neural networks
No full textToday biometric identity authentication technologies are widespread. These systems are implemented not only in enterprises, controlled-access facilities, but also on smartphones of ordinary users and in online applications. The problem of choosing one of the authentication methods remains urgent. This paper provides a comparative analysis of existing systems and concludes that one of the most common and persistent methods is facial authentication system. The most powerful types of attacks on the biometric system are attacks on the database of biometric templates and attacks on sensors for obtaining biometric characteristics. Attacks on biometric sensors or spoofing attack is aimed at impersonating another person through fake biometric data. The paper deals with the possibility of special attacks on the biometric system of authentication by face image. A new method of detecting fake attacks (spoofing attacks) is proposed. The method is based on the use of an artificial convolutional neural network which was trained using a Replay-Attack Database from Idiap Research Institute. The obtained results show high efficiency of the proposed method of detecting spoofing attacks: the probability that an attack will be detected is 94.98%
Investigation of the Correlation Properties of Spreading Sequences for Asynchronous CDMA
No full textAsynchronous CDMA uses sets of spreading sequences (eg, Gold codes, Kasami sequences, etc.) that are statistically uncorrelated for arbitrarily random starting points. We study various sets of spreading sequences and evaluate their correlation properties. The Fundamental Welch Bound sets the limit below which the crosscorrelation square between any cyclically shifted sequence copies cannot fall. We estimate the correlation for different spreading sequences in relation to this theoretical limit. For this we use a polynomial approximation (by Laurent and Puiseux series). We also consider large sets of spreading sequences, for which the correlation modulus increases by about 2 times (compared to Gold codes). However, in comparison with Gold codes, the cardinality of large sets is increased by more than N times (where N is the length of the sequence). This is an advantage that will significantly increase the capacity of asynchronous CDMA and reduce the cost of communication services. In addition, new sets of spreading signals will be useful for the implementation of the so-called soft capacity, i.e. when, if necessary, the base station can increase the subscriber capacity with a slight decrease in the quality of service
Method of Tabular Implementation for Diagnostics of Non-Positional Code Structures in the System of Residual Classes
No full textIn the article is proposed a method of the tabular implementation of the procedure for diagnosing data that are presented in the system of residual class (SRC). It is shown that the main disadvantage of the existing methods for diagnosing data in SRC is the considerable time of diagnosing data. The method of the tabular implementation of the procedure for diagnosing data in the SRC presented in the article makes it possible to reduce the time of the diagnostic procedure. Compared with the known methods the data diagnosis time is reduced due to the following factors. Firstly, due to the exceptions of the procedure of converting numbers from the SRC to the positional binary numeral system, i.e. exceptions from the chain of operations of positional comparison of numbers. Secondly, the data diagnostics time is reduced on the decrease of the number of SRC bases which сan cause an error. Finally, thirdly, the data diagnostics time is reduced due to the use of a tabular sample of the value of an alternative set of numbers in the SRC, practically in one machine cycle. It is given a geometric interpretation of the proposed method of tabular implementation of the procedure for diagnosing data, which is presented in the SRC. Also, we gave the examples of using the proposed method for diagnosing data for a specific SRC. Thus, the proposed method makes it possible to reduce the time for diagnosing data errors presented in the SRC, which increases the efficiency of diagnosing non-positional code structures
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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