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Abstract:Face spoofing is considered to be one of the prominent threats to face recognition systems. However, in order to improve the security measures of such biometric systems against deliberate spoof attacks, liveness detection has received significant recent attention from researchers. For this purpose, analysis of facial skin texture properties becomes more popular because of its limited resource requirement and lower processing cost. The traditional method of skin analysis for liveness detection was to use Local Binary Pattern (LBP) and its variants. LBP descriptors are effective, but they may exhibit certain limitations in near uniform patterns. Thus, in this paper, we demonstrate the effectiveness of Local Ternary Pattern (LTP) as an alternative to LBP. In addition, we adopted Dynamic Local Ternary Pattern (DLTP), which eliminates the manual threshold setting in LTP by using Weber's law. The proposed method was tested rigorously on four facial spoof databases: three are public domain databases and the other is the Universiti Putra Malaysia (UPM) face spoof database, which was compiled through this study. The results obtained from the proposed DLTP texture descriptor attained optimum accuracy and clearly outperformed the reported LBP and LTP texture descriptors.

Data protection from malicious attacks and misuse has become a crucial issue. Various types of data, including images, videos, audio and text documents, have given cause for the development of different methods for their protection. Cryptography, digital signatures and steganography are the most well known technologies used to protect data. During the last decade, digital watermarking technology has also been utilized as an alternative to prevent media forgery and tampering or falsification to ensure both copyright and authentication. Much work has been done to protect images, videos and audio but only a few algorithms have been considered for text document protection with digital watermarking. However, our survey observed that available text watermarking algorithms are neither robust nor imperceptible and as such remain unsecured methods of protection. Hence, research to improve the performance of text watermarking algorithms is required. This paper reviews current watermarking algorithms for text documents and categorizes text watermarking methods based on the way the text was treated during the watermarking process. It also discusses merits and demerits of available methods as well as recent proposed methods for evaluating text watermarking systems and the need for further research on digital text watermarking.

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.

This article presents an analysis of handwritten signature dynamics belonging to two authentication groups, namely genuine and forged signature samples. Genuine signatures are initially classified based on their relative size, graphical complexity, and legibility as perceived by human examiners. A pool of dynamic features is then extracted for each signature sample in the two groups. A two-way analysis of variance (ANOVA) is carried out to investigate the effects and the relationship between the perceived classifications and the authentication groups. Homogeneity of variance was ensured through Bartlett's test prior to ANOVA testing. The results demonstrated that among all the investigated dynamic features, pen pressure is the most distinctive which is significantly different for the two authentication groups as well as for the different perceived classifications. In addition, all the relationships investigated, namely authenticity group versus size, graphical complexity, and legibility, were found to be positive for pen pressure.